biology classification assignment

The Biology Corner

Biology Teaching Resources

Practice with Taxonomy

Though Next Generation Science Standards does not emphasize the memorizing of major groups anymore, students can benefit from learning the basics of how animals are classified. Taxonomy is often introduced with evolution, where students learn how to analyze phylogenetic trees and create cladograms .

This worksheet is a simple reinforcement exercise that covers the six kingdoms and the classification system developed by Carolus Linnaeus. I teach my students to learn this system with the mnemonic “ K ing P hilip C ame O ver F or G reat S oup” though there are many other versions. Each letter corresponds with a level of classification: Kingdom, Phylym, Class, Order, Family Genus, Species. You can add “darling” to the front of the sentence if you also want to include DOMAIN.

What I’ve found is that students can memorize this sequence, but they struggle with the actual concepts. Many will not be able to explain why there are more individual species within a class than there are within an order. Hierarchies can be difficult. I often uses boxes of varies sizes to show how each level fits into the next one.

My slides for classification are shown below. They are Google slides and can be edited if you make a copy.

Shannan Muskopf

school Campus Bookshelves
menu_book Bookshelves
perm_media Learning Objects
login Login
how_to_reg Request Instructor Account
hub Instructor Commons

Margin Size

Download Page (PDF)
Download Full Book (PDF)
Periodic Table
Physics Constants
Scientific Calculator
Reference & Cite
Tools expand_more
Readability

selected template will load here

This action is not available.

1.7: Assignment- Visualizing Taxonomy

Last updated
Save as PDF
Page ID 44282

$ \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } $

$ \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} $

$ \newcommand{\id}{\mathrm{id}}$ $ \newcommand{\Span}{\mathrm{span}}$

( \newcommand{\kernel}{\mathrm{null}\,}\) $ \newcommand{\range}{\mathrm{range}\,}$

$ \newcommand{\RealPart}{\mathrm{Re}}$ $ \newcommand{\ImaginaryPart}{\mathrm{Im}}$

$ \newcommand{\Argument}{\mathrm{Arg}}$ $ \newcommand{\norm}[1]{\| #1 \|}$

$ \newcommand{\inner}[2]{\langle #1, #2 \rangle}$

$ \newcommand{\Span}{\mathrm{span}}$

$ \newcommand{\id}{\mathrm{id}}$

$ \newcommand{\kernel}{\mathrm{null}\,}$

$ \newcommand{\range}{\mathrm{range}\,}$

$ \newcommand{\RealPart}{\mathrm{Re}}$

$ \newcommand{\ImaginaryPart}{\mathrm{Im}}$

$ \newcommand{\Argument}{\mathrm{Arg}}$

$ \newcommand{\norm}[1]{\| #1 \|}$

$ \newcommand{\Span}{\mathrm{span}}$ $ \newcommand{\AA}{\unicode[.8,0]{x212B}}$

$ \newcommand{\vectorA}[1]{\vec{#1}} % arrow$

$ \newcommand{\vectorAt}[1]{\vec{\text{#1}}} % arrow$

$ \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } $

$ \newcommand{\vectorC}[1]{\textbf{#1}} $

$ \newcommand{\vectorD}[1]{\overrightarrow{#1}} $

$ \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} $

$ \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} $

Open Pedagogy Assignments are assignments in which students use their agency and creativity to create knowledge artifacts that can support their own learning, their classmates’ learning, and the learning of students around the world. (See this peer-reviewed article for more details.) The assignment on this page is aligned to a learning outcome of Biology for Non-Majors I and we’ve identified the module where the reading appears. The assignment can be created with basic web and computing tools, a cell phone camera or any video recording device, Google or Word documents, and your learning management system.

Learning Objectives

Explain how relationships are indicated by the binomial naming system

In the introductory module on defining biology and applying its principles, we provide a general overview of how and why biologists study life. For this assignment, you are going to reflect upon how biologists organize and classify the different forms of life so that we can better understand them.

The product of your work may help future students to learn about some of the most difficult concepts in the course. Thus, think of your audience as friends who are taking Biology for Nonmajors in the next term. Your objective is to help them understand the classification systems that biologists use by visualizing the concept.

First , choose an organism to identify using biological taxonomy. This can be any plant or animal that you find interesting, peculiar, or from your experience. Think again about the future students who may be seeing your visualization: can you select an organism from your local environment or that has particular meaning from students that attend your institution?

Second , identify the scientific name in binomial nomenclature of the organism, as well as the series of classifications that make up the taxonomic hierarchy for your organism. According to the system proposed by Carl Linnaeus there are eight taxons, listed here from the broadest to the most specific:

Third , how would you visualize this organism’s taxonomy? Someone viewing the visualization should be able to understand hierarchical or nested relationships of the taxonomic groupings. You can use any means you wish to make your original visualization: draw, collage, digital illustration, video, slideshow, and so on. A successful visualization will include clear and accurate labels.

Lastly , share the visualization with your instructor. After grading and with your permission, your visualization may appear in future sections of the course to improve other students’ learning!

A Note To Teachers: The first time your students complete this assignment, choose the best ones, and ask students for permission to include them in future sections. Just post the visualizations in the appropriate module in the LMS. The idea is to have students generate content that other students can learn from in this assignment.

Browse Course Material

Course info, instructors.

Dr. Claudette Gardel
Prof. Eric Lander
Prof. Robert Weinberg
Prof. Andrew Chess

Departments

As taught in, learning resource types, introduction to biology, assignments.

You are leaving MIT OpenCourseWare

About Serendip Studio
Facts about Serendip
For Our First Time Visitors
A Guided Tour
What's New (edited)
What's New (full)
Bookshelves
Serendip A to Z
Serendip Readers Write Back
Founded in 1994

Search Serendip

a digital ecosystem, fueled by serendipity

Diablogging about Games
The Breaking Project
Assessing Assessment
Empowering Learners Handbook
Brain and Education
Pedagogical Discussions
For College Faculty
For K-12 Teachers
Minds-on Activities for Teaching Biology
Next Generation Science Standards
Remote Ready Biology Activities
NGSS Biology Activities
Hands-On Activities for Teaching Biology
Teaching Climate Change
Science Education
Summer Institutes for K-12 Teachers 1995-2010
Nervous System Basics
Neurobiology of Vision
"Reality"
Variability, Diversity, and Creativity
Exploration & Understandings
Mind, Body & the Self?
Social Organization
Mental Health
Growing Points
General Biology
Neurobiology
Emergence & Complex Systems
Introduction to Biology
Neurobiology & Behavior
The Story of Evolution & the Evolution of Stories
Mental Health & the Brain
Biodiversity
What is Science?
Science and the Arts & Humanities
Science and Philosophy
Science and Religion
Science and Social Organization
Science as a Professional Activity
Biology & Evolution
Interdisciplinary Course Archive
Digital Humanities Resources
On Beyond Webpapers
Student Projects
Art Exhibitions

A Random Walk

Story Telling
Making Sense of Understanding
Coordination without a Leader
Chance in Life and the World
Ambiguous Figures
Hofstadter's Road Sign
Playground Activities
Emergence Models

Serendip is an independent site partnering with faculty at multiple colleges and universities around the world. Happy exploring!

Hands-on Activities for Teaching Biology to High School and Middle School Students

Hands-on Biology Activities

biological molecules
cell structure and function
cellular respiration and photosynthesis
cell division and genetics
molecular biology
evolution and ecology
human physiology .

The expression "hands-on, minds-on" summarizes the philosophy we have incorporated in these activities - namely, that students will learn best if they are actively engaged and if their activities are closely linked to understanding important biological concepts.

Most of our activities support the Next Generation Science Standards (NGSS).

Additional resources for teaching biology are available at Minds-on Activities for Teaching Biology . These teaching resources include remote ready analysis and discussion activities , games, and overviews of important biological topics, including major concepts, common misconceptions, and suggested learning activities. We encourage you to subscribe to our listserv to receive notices when we post new activities or significantly improved versions of current activities.

We invite general comments in this comments section. If you have comments on specific activities, please use the links above to get to the comments section for each activity. We welcome suggestions for other teachers, including useful preparatory or follow-up activities, additional resources, any questions you have, or a brief description of any problem you might have encountered.

If you would prefer to send your comments or questions in a private message, please write Ingrid Waldron at [email protected] .

As a first year IB Biology teacher in Australia, I am very grateful for all of Ingrid's hands-on, minds-on activities. I love how they link to real-world scenarios, add meaning and foster critical thinking. Thank you so much!

I am a public high school science teacher, and I've used many of your materials. When I need something new, I often go to your site first because you have such high-quality, hands-on, minds-on, NGSS-aligned activities.

Thank you for creating these resources and for making them free. These resources are needed and much appreciated!

I've been a big fan of your Serendip Studio resources for years! I've used many of your activities in my Biology and my AP Biology classes. Your site is always one of the top 3 that I mention to other Biology teachers when they ask for recommendations of great websites. Thank you so much for your dedication to Biology education.

I am a high school Biology teacher and have been for ten years. I just wanted to thank you for making your amazing materials so accessible. It is very rare to find activities that are so accurate and comprehensive. I rarely need to modify the activities but it is helpful to be able to given the word document format. Thank you! Thank you! Thank you!

I am training to be science teacher and will graduate next year (2015) and I find very helpful in retrieving your lab practicals. I have retrieved many and hope it will help me much in my teaching career. Thank you very much you producing this practicals.

Science and PBL

Hi! I am the Chair of Science of an alternative learning school that focuses on Project Based Learning Curriculum. This is by far and away the best website that I have ever seen, in all of my years of teaching, for Science PBL Tasks. Thank you very much for your assistance. And please notify me of updates.

Biology. AP Biology, Forensic, Anatomy

I teach four HS sciences and found your activities to be a wonderful addition to my curriculum in all four areas! They provide excellent reinforcement and depth to the content we are studying. Your activities truly help the students learn and have saved me a lot of time in preparation. I highly recommend these activities. THANK YOU and well done.

This is my first year teaching Biology, and as I work to incorporate more meaningful labs into my curriculum, your site has been so very valuable! I especially appreciate how your labs include the scientific method so thoroughly and allow for student design of experiments! Also thank you for providing the word documents to allow teachers like me to edit the labs to fit our specific groups of students! Keep up the good work!

I am a Community College instructor on the prowl for activities and resources to incorporate into our Intro Biology lab. I have found wonderful stuff here and have used the Sockosome Meiosis activity with great success.

I do have a question: What is your policy regarding distribution of printed materials? I would like to incorporate some of your content into a lab manual to post online for our students to access. This would save them money and save us the hassle of dealing with publishers and the college bookstore. The copyright to our current manual is owned by the publisher (though we created it) so we need to come up with new material. What are your terms of use?

Ricardo Azpiroz, PhD Richland College Dallas, TX

Using our material in an online lab manual

Dear Ricardo,

We are happy to learn that you have found our material useful.

If you want to post some of our materials in a lab manual on a website with access restricted to students in your courses, you are free to use any of our material, provided you acknowledge the source, including authors and website.

If you want to post any of our materials in a lab manual on a website with public access, we request that, instead of copying our material, you provide a brief explanation with a link to the relevant part of the Serendip website.

Please let me know if you would like any additional information.

Ingrid Waldron, Ph.D.

Department of Biology University of Pennsylvania

Post new comment

Download minds-on activities for teaching biology.

Introduction and Activities Listing

Intro and Biological Molecules

Characteristics of Life
Levels of Organization in Biology (NGSS)
Introduction to Proteins and DNA (NGSS)
Enzymes Help Us Digest Food (NGSS; hands-on)
A Scientific Investigation – What types of food contain starch and protein? (NGSS; hands-on)
Coronaviruses – Introduction (NGSS)
Who Took Jerell’s iPod? -- An Organic Compound Mystery (hands-on)
Is Yeast Alive? (hands-on)
Macromolecules Jeopardy

Cell Structure and Function

Cell Structure and Function – Major Concepts and Learning Activities
Introduction to Cells (NGSS)
Structure and Function of Cells, Organs and Organ Systems (NGSS)
Why do some plants grow in odd shapes? (NGSS)
Introduction to Osmosis (NGSS; hands-on)
Cell Membrane Structure and Function (NGSS; hands-on)
Cell Vocabulary Review Game

Cellular Respiration and Photosynthesis

Cellular Respiration and Photosynthesis - Key Concepts and Activities
How do organisms use energy? (NGSS)
Using Models to Understand Cellular Respiration (NGSS)
Using Models to Understand Photosynthesis (NGSS)
Photosynthesis, Cellular Respiration and Plant Growth (NGSS; hands-on)
Food, Energy and Body Weight (NGSS)
How do muscles get the energy they need for athletic activity? (NGSS)
Alcoholic Fermentation in Yeast – A Bioengineering Design Challenge (NGSS; hands-on)
Photosynthesis and Cellular Respiration (NGSS)
Where does a tree’s mass come from? (NGSS)
Photosynthesis Investigation (NGSS; hands-on)

Cell Division

Mitosis and the Cell Cycle (NGSS; hands-on)
Mitosis and the Cell Cycle (NGSS)
Meiosis and Fertilization – Understanding How Genes Are Inherited (NGSS; hands-on)
Understanding How Genes are Inherited via Meiosis and Fertilization (NGSS)
Comparing Mitosis and Meiosis
What causes melanoma and other types of cancer? (NGSS)
Mistakes in Meiosis – Down Syndrome or Embryo Death (NGSS)
Mitosis, Meiosis and Fertilization Vocabulary Review Game
Genetics Concepts and Activities (NGSS)
Genetics (NGSS; hands-on)
Genetics Intro – Family Members (NGSS)
Genetics Sickle Cell Anemia and Trait (NGSS)
Genetics Probability – Sex Ratios (NGSS)
Mistake in copying DNA & Dwarfism (NGSS)
Soap Opera Genetics (NGSS)
Were the babies switched? The Genetics of Blood Types (NGSS; hands-on)
Dragon Genetics I (hands-on)
Dragon Genetics II (hands-on)
Learning about Genetic Disorders
Genetics Vocabulary Review Game
Genetics Jeopardy

Molecular Biology

Molecular Biology: Major Concepts and Learning Activities (NGSS)
DNA (NGSS; hands-on)
DNA Function, Structure and Replication (NGSS)
How Genes Can Cause Disease - Introduction to Transcription and Translation (NGSS; hands-on)
How Genes Can Cause Disease - Understanding Transcription and Translation (NGSS)
UV, Mutations and DNA Repair (NGSS; hands-on)
What types of mutations cause more vs. less severe muscular dystrophy? (NGSS)
Cell Differentiation and Epigenetics (NGSS)
Genetic Engineering Challenge – Preventing Vitamin A Deficiency (NGSS)
Gene Editing with CRISPR-Cas – Potential Sickle Cell Anemia Cure (NGSS)
Molecular Biology Vocabulary Review Game
Resources for Teaching and Learning about Evolution
Evolution by Natural Selection (NGSS; hands-on)
What is natural selection? (NGSS)
Natural Selection and the Peppered Moth (NGSS)
How have mutations and natural selection affected fur color in mice? (NGSS)
How Whales Evolved (NGSS)
How Eyes Evolved – Analyzing the Evidence (NGSS)
How does evolution result in similarities and differences? (NGSS; hands-on)
What is a species? (NGSS)
Coronavirus Evolution and the COVID-19 Pandemic (NGSS)
Evolution and Adaptations (NGSS)
Ecology Concepts and Learning Activities (NGSS)
Exponential and Logistic Population Growth Models vs. Complex Reality (NGSS)
Some Similarities between the Spread of Infectious Disease and Population Growth (NGSS; hands-on)
Stability and Change in Biological Communities (NGSS)
Food Webs, Energy Flow, Carbon Cycle and Trophic Pyramids (NGSS)
Food Webs (NGSS)
Carbon Cycles and Energy Flow through Ecosystems (NGSS)
Trophic Pyramids (NGSS)
Introduction to Global Warming (NGSS)
Food and Climate Change – How can we feed a growing world population without increasing global warming? (NGSS)
Resources for Teaching about Climate Change
The Ecology of Lyme Disease (NGSS)

Human Physiology and Health

Negative Feedback, Homeostasis, and Positive Feedback – Examples and Concepts (NGSS; hands-on)
Homeostasis, Negative Feedback, and Positive Feedback (NGSS)
How do we Sense the Flavors of Food? (NGSS; hands-on)
COVID-19 Vaccines (NGSS)
How to Reduce the Spread of COVID-19 (NGSS)
Molecular and Evolutionary Biology of HIV/AIDS and Treatment (NGSS)
Resources for Teaching Cancer Biology
Carbohydrate Consumption, Athletics, Health – Using Science Process Skills
Vitamins and Health – Why Experts Disagree
Regulation of Human Heart Rate (hands-on)
Should You Drink Sports Drinks? When? Why?

Subscribe to our listserv to receive notices when we post new activities or significant revisions.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License . To preserve the value of these learning activities for other teachers, please do not post keys for any questions from any of these activities!

Remote Ready Biology Learning Activities

Scanning electron microscope image of SARS-CoV-2

Remote Ready Biology Learning Activities has 50 remote-ready activities, which work for either your classroom or remote teaching.

What's New? Subscribe to Serendip Studio

The Evolution of "Evolution (n.)"
Leah and Michel
The Three Doors of Serendip Applet 3
Women rights ~
Observations of Teknolust
Minds-on Activities for Teaching Biology 4 weeks 3 days ago
Gene Editing with CRISPR-Cas – A Potential Cure for Severe Sickle Cell Anemia 4 weeks 3 days ago
Resources for Teaching and Learning about Evolution 8 weeks 5 days ago
Resources for Teaching about Coronavirus 9 weeks 1 day ago
Introduction to Cells 9 weeks 3 days ago

SUBSCRIBE >

Science Notes Posts
Contact Science Notes
Todd Helmenstine Biography
Anne Helmenstine Biography
Free Printable Periodic Tables (PDF and PNG)
Periodic Table Wallpapers
Interactive Periodic Table
Periodic Table Posters
How to Grow Crystals
Chemistry Projects
Fire and Flames Projects
Holiday Science
Chemistry Problems With Answers
Physics Problems
Unit Conversion Example Problems
Chemistry Worksheets
Biology Worksheets
Periodic Table Worksheets
Physical Science Worksheets
Science Lab Worksheets
My Amazon Books

Biology Worksheets, Notes, and Quizzes (PDF and PNG)

This is a collection of free biology worksheets, notes, handouts, slides, study guides and quizzes. Most content targets high school, AP biology, genetics, anatomy/physiology, immunology, and biology 101 and 102 in college. There is also biochemistry and physics for biologists. However, some resources are at the grade school and middle school level.

The files are PDF, PNG, JPG, and formats using Google Apps for Google Classroom. Most of the time, these formats are interchangeable. So, if you see something you like, but want a different format, just let us know. Print these resources, make transparencies and slides, etc.

In the interest of quick load time, not all of the images are shown. If you’d rather see them all, just contact us!

Biochemistry

Understanding the Differences Between RNA and DNA - Worksheet

[ Google apps worksheet ][ worksheet PDF ][ answers PDF ][ worksheet PNG ][ answers PNG ]

Enzymes Definitions

[ Google Slides worksheet ][ worksheet PDF ][ answers PDF ][ worksheet PNG ][ answers PNG ]

20 Amino Acids [ PNG ][ PDF ]
Amino Acid Side Chains [ PNG ][ PDF ]
Identifying Type of Biological Macromolecules [ Google Slides worksheet ][ worksheet PDF ][ answers PDF ][ worksheet PNG ][ answers PNG ]
Disaccharide Examples [ PNG ]
Products of Photosynthesis [ JPG ]
Anabolism vs Catabolism [ PNG ]
3 Parts of a Nucleotide [ PNG ]
Fermentation Definition and Examples [ PNG ]

General and Cell Biology

Major Organelles and Their Function Worksheet

Organelles and Their Functions

Parts of a Plant Cell

Label Parts of a Chloroplast

[ Google Apps worksheet ][ worksheet PDF ][ answers PDF ][ worksheet PNG ][ answers PNG ]

Label Parts of a Mitochondria

Label the Animal Cell

[ Google Apps worksheet ][ worksheet PDF ][ worksheet PNG ][ answers PNG ]

Prokaryotes vs Eukaryotes Worksheet

Steps of the Cell Cycle

Steps of Mitosis

Membrane Transport Terms and Definitions

Membrane Transport Worksheet #2

The Plasma Membrane

Label a Bacterial Cell

Label a Bacteriophage [ Google Apps worksheet ][ worksheet PDF ][ worksheet PNG ][ answers PNG ]
Evidence of Evolution Worksheet [ Google Apps worksheet ][ Worksheet PDF ][ Worksheet PNG ][ Answers PNG ]
Evolutionary Processes Worksheet [ worksheet Google Apps ][ worksheet PDF ][ worksheet PNG ][ answers PNG ]
Major Receptor Families [ Google Apps worksheet ][ worksheet PDF ][ worksheet PNG ][ answers PNG ]
Label a Bacterial Cell Membrane ( E. coli ) [ Google Apps worksheet ][ worksheet PDF ][ worksheet PNG ][ answers PNG ]

Anatomy and Physiology

These worksheets are only a portion of the available anatomy and physiology worksheets. Human anatomy and physiology worksheets have their own section.

Label the Heart

Label the Eye

[ Google Apps worksheet ][ worksheet PDF ][ answers PDF ][ worksheet PNG ]

Types of Blood Cells

[ worksheet Google Apps ][ worksheet PDF ][ worksheet PNG ][ answers PNG ]

Label the Muscles

[ worksheet PDF ][ worksheet PNG ][ answers PNG ]

Label the Ear

[ Google Apps worksheet ][ Worksheet PDF ][ Worksheet PNG ][ Answers PNG ]

Label the Lungs

Label the Kidney

Label the Liver

Anatomy of the Large Intestine Worksheet

Label the Large Intestine

Label the Stomach

[ Google Apps worksheet ] [Worksheet PDF ][ Worksheet PNG ][ Answers PNG ]

External Nose Anatomy

[ Worksheet PDF ][ Worksheet Google Apps ][ Worksheet PNG ][ Answers PNG ]

Parts of the Nose

Label Bones of the Skeleton

Label the Lymph Node

Label the Parts of the Brain

Label the Lobes of the Brain

Anatomical Directions of the Brain Worksheet

Brain Anatomical Sections

Arteries of the Brain

Label the Pancreas

Label the Spleen

Label the Digestive System

Label the Respiratory System

Parts of a Neuron

Label the Lips

Label the Skin

Label the Circulatory System

The Urinary Tract

[ Worksheet PDF ][ Worksheet Google Apps ][ Worksheet PNG ][ Answer Key PNG ]

The Bladder

The Female Reproductive System [ worksheet PDF ][ worksheet Google Apps ][ worksheet PNG ][ answers PNG ]

Parts of a Flower

Label the Orchid Plant

[ Worksheet PDF ][ Worksheet Google Apps ][ Worksheet PNG ] [Answer Key PNG ]

Parts of an Orchid Flower

Parts of a Monocot Seed

Parts of a Fern

Parts of a Tree Trunk

Parts of a Tree

[ worksheet PDF ][ worksheet Google Apps ][ worksheet PNG ][ answers PNG ]

Parts of a Mushroom

Label the Shark

Label the Fish

Parts of a Bird

Bird Anatomy

Frog Life Cycle

Parts of a Mosquito (Insect)

Bones of the T. rex Skull

[ worksheets PDF ][ worksheet Google Slides ][ worksheet PNG ][ answers PNG ]

Holes of the T. rex Skull

Label the T. rex Skeleton [ worksheets PDF ][ worksheet Google Slides ][ worksheet PNG ][ answers PNG ]
Label Human Teeth [ Worksheet PDF ][ Worksheet Google Apps ][ Worksheet PNG ][ Answer Key PNG ]
Monocot vs Dicot Seeds [ worksheet PDF ][ worksheet Google Slides ][ worksheet PNG ][ answers PNG ]
Label the Moss [ worksheet PDF ][ worksheet Google Slides ][ worksheet PNG ][ answers PNG ]
Diagram of the Human Eye [ JPG ]

Use a completed worksheet as a study guide.

Cells of the Immune System

Immune Cell Functions

[ worksheet Google Apps ][ worksheet PDF ][ worksheet PNG #1][ answers PNG #1][ worksheet PNG #2][ answers PNG #2]

Methods to Study Virus Structures

[ worksheet Google Slide ][ worksheet PDF ][ worksheet PNG ][ answers PNG ]

Icosahedral Virus Capsids

Human DNA Viruses

Human RNA Viruses

This is selection of worksheets relating to DNA, RNA, transcription, translation, genetic crosses, plasmid mapping, etc. See the full collection of genetics worksheets if you’re don’t see what you need.

DNA Replication

Types of Mutations

Monohybrid Cross Worksheet #1

Monohybrid Cross Worksheet #2

Monohybrid Cross Worksheet #3

Monohybrid Cross #4 – Multiple Alleles

Monohybrid Cross Worksheet #5: Multiple Alleles [ worksheet Google Apps ][ worksheet PDF ][ worksheet PNG ][ answers PNG ]

Monohybrid Cross #6 – Sex-Linked Inheritance

Monohybrid Cross #7 – Sex-Linked Inheritance

Dihybrid Cross Worksheet #1

Dihybrid Cross 2 - Worksheet (8.5 × 11 in)

Dihybrid Cross Worksheet #2

Dihybrid Cross 3 - Student (8.5 × 11 in)

Dihybrid Cross Worksheet #3

Dihybrid Cross 4 - Student (8.5 × 11 in)

Dihybrid Cross Worksheet #4

Dihybrid Cross #5 – Epistasis

Dihybrid Cross #6 – Epistasis

Incomplete Dominance Worksheet #1

Incomplete Dominance Worksheet #2

Natural Selection Worksheet

Convergent vs Divergent Evolution Worksheet

Intro to Pedigrees Worksheet #1

Pedigrees/Genealogy Worksheet #2

Pedigrees/Genealogy Worksheet #3 – X-Linked Dominant Traits

Pedigree Worksheet 4 X-Linked Recessive Traits (Student)

Pedigrees/Genealogy Worksheet #4 – X-Linked Recessive Traits

Pedigree Worksheet 5 Autosomal Dominant Traits (Student)

Pedigrees/Genealogy Worksheet #5 – Autosomal Dominant Traits

Pedigrees/Genealogy Worksheet #6 – Autosomal Recessive Traits

Pedigrees/Genealogy Worksheet #7 – Comprehension Skills

Pedigrees/Genealogy Worksheet #8 – Identifying Inheritance Patterns

Autosomal vs Sex-Linked Inheritance

Plasmid Mapping

Genotype vs Phenotype [ PNG ]
Genetic Codon Chart [ PNG ][ PDF ]
RNA vs DNA [ JPG ]

Use labelled diagrams as study guides.

Ecosystems Worksheet

Levels of organization in ecology Worksheet

Levels of Organization (Ecosystem)

Primary Succession

Secondary Succession

Label the Insect

Label the Bee

Chicken Life Cycle (Basic)

Bird Life Cycle (Basic)

Butterfly Life Cycle

Biotic and Abiotic Factors [ worksheet PDF ][ worksheet Google Apps ][ worksheet PNG ][ answers PNG ]
Kingdoms of Life Graphic [ PNG ]
Autotroph vs Heterotroph Graphic [ PNG ]
Commensalism Definition and Examples [ PNG ]
Difference Between Butterflies and Moths [ JPG ]
Difference Between Bugs and Insects [ PNG ]

Parasitology

Schistosome Life Cycle

Schistosome Anatomy

Giardia Life Cycle [ worksheet Google Apps ][ worksheet PDF ][ worksheet PNG ][ answers PNG ]

Physical Science for Biology

There is also an entire section devoted to physical science worksheets and study guides.

Adhesion vs Cohesion Graphic [ PNG ]
What Is Entropy? [ PNG ]
Freezing Point of Water [ PNG ]

Biology Labs

How to Extract DNA From a Banana [ PNG ]

Biology Word Search Puzzles

DNA Replication Word Search
Gel Electrophoresis Word Search
Citrus Fruits
General Biology Word Search Puzzle [ PNG ]
Life Science Word Search [ PNG ][ PDF ]
Cell Biology Word Search [ PNG ][ PDF ]
Amino Acid Word Search [ PNG ][ PDF ]
Biome Word Search [ PNG ][ PDF ]
Environmental Science Word Search [ PNG ][ PDF ]
Photosynthesis Word Search [ PNG ]
Human Skeleton Bones Word Search [ PNG ]
Dinosaur Word Search [ PNG ][ PDF ]
Different Dinosaur Word Search [ PNG ][ PDF ]
Wild Cats Word Search [ PNG ][ PDF ]
Shark Word Search [ PNG ]
Trees Word Search [ PNG ][ PDF ]
Flowers Word Search [ PNG ][ PDF ]
Butterfly Word Search [ PNG ][ PDF ]
Genetics Word Search [ PNG ][ PDF ]

Miscellaneous Biology Notes and Resouces

How Long Can Germs Live on Surfaces? [ PNG ]
10 Radioactive Foods [ PNG ]
Examples of Organic Compounds [ PNG ]

Biology Notes Terms of Use

You are welcome to print these resources for personal or classroom use. They may be used as handouts or posters. They may not be posted elsewhere online, sold, or used on products for sale.

This page doesn’t include all of the assets on the Science Notes site. If there’s a table or worksheet you need but don’t see, just let us know.

Faculty Resources

Assignments.

The assignments in this course are openly licensed, and are available as-is, or can be modified to suit your students’ needs. Sample completed assignments are available to faculty who adopt Waymaker, OHM, or Candela courses with paid support from Lumen Learning. This approach helps us protect the academic integrity of these materials by ensuring they are shared only with authorized and institution-affiliated faculty and staff.

This course provides 10 prompts and grading rubrics for written assignments, designed to align with course outcomes. You can view them by clicking on the assignment links in course modules.

If you import this course into your learning management (Blackboard, Canvas, etc.), the assignments will automatically be loaded into the assignment tool. You can view them below or throughout the course.

Assignments. Provided by : Lumen Learning. License : CC BY: Attribution
Pencil Cup. Authored by : IconfactoryTeam. Provided by : Noun Project. Located at : https://thenounproject.com/term/pencil-cup/628840/ . License : CC BY: Attribution

Biology Article

Cells are the basic, fundamental unit of life. So, if we were to break apart an organism to the cellular level, the smallest independent component that we would find would be the cell.

Explore the cell notes to know what is a cell, cell definition, cell structure, types and functions of cells. These notes have an in-depth description of all the concepts related to cells.

Table of Contents

Cell Definition

What is a cell, characteristics of cells, types of cells, cell structure, cell theory.

Functions of a Cell

Cells are the fundamental unit of life. They range in size from 0.0001 mm to nearly 150 mm across.

“A cell is defined as the smallest, basic unit of life that is responsible for all of life’s processes.”

Cells are the structural, functional, and biological units of all living beings. A cell can replicate itself independently. Hence, they are known as the building blocks of life .

Each cell contains a fluid called the cytoplasm, which is enclosed by a membrane. Also present in the cytoplasm are several biomolecules like proteins, nucleic acids and lipids. Moreover, cellular structures called cell organelles are suspended in the cytoplasm.

A cell is the structural and fundamental unit of life. The study of cells from its basic structure to the functions of every cell organelle is called Cell Biology. Robert Hooke was the first Biologist who discovered cells.

All organisms are made up of cells. They may be made up of a single cell (unicellular), or many cells (multicellular). Mycoplasmas are the smallest known cells. Cells are the building blocks of all living beings. They provide structure to the body and convert the nutrients taken from the food into energy.

Cells are complex and their components perform various functions in an organism. They are of different shapes and sizes, pretty much like bricks of the buildings. Our body is made up of cells of different shapes and sizes.

Cells are the lowest level of organisation in every life form. From organism to organism, the count of cells may vary. Humans have more number of cells compared to that of bacteria .

Cells comprise several cell organelles that perform specialised functions to carry out life processes. Every organelle has a specific structure. The hereditary material of the organisms is also present in the cells.

Discovery of Cells

Discovery of cells is one of the remarkable advancements in the field of science. It helps us know that all the organisms are made up of cells, and these cells help in carrying out various life processes. The structure and functions of cells helped us to understand life in a better way.

Who discovered cells?

Robert Hooke discovered the cell in 1665. Robert Hooke observed a piece of bottle cork under a compound microscope and noticed minuscule structures that reminded him of small rooms. Consequently, he named these “rooms” as cells. However, his compound microscope had limited magnification, and hence, he could not see any details in the structure. Owing to this limitation, Hooke concluded that these were non-living entities.

Later Anton Van Leeuwenhoek observed cells under another compound microscope with higher magnification. This time, he had noted that the cells exhibited some form of movement (motility). As a result, Leeuwenhoek concluded that these microscopic entities were “alive.” Eventually, after a host of other observations, these entities were named as animalcules.

In 1883, Robert Brown, a Scottish botanist, provided the very first insights into the cell structure. He was able to describe the nucleus present in the cells of orchids.

Following are the various essential characteristics of cells:

Cells provide structure and support to the body of an organism.
The cell interior is organised into different individual organelles surrounded by a separate membrane.
The nucleus (major organelle) holds genetic information necessary for reproduction and cell growth.
Every cell has one nucleus and membrane-bound organelles in the cytoplasm.
Mitochondria, a double membrane-bound organelle is mainly responsible for the energy transactions vital for the survival of the cell.
Lysosomes digest unwanted materials in the cell.
Endoplasmic reticulum plays a significant role in the internal organisation of the cell by synthesising selective molecules and processing, directing and sorting them to their appropriate locations.

Prokaryotic Cells

Main article: Prokaryotic Cells

Prokaryotic cells have no nucleus. Instead, some prokaryotes such as bacteria have a region within the cell where the genetic material is freely suspended. This region is called the nucleoid.
They all are single-celled microorganisms. Examples include archaea, bacteria, and cyanobacteria.
The cell size ranges from 0.1 to 0.5 µm in diameter.
The hereditary material can either be DNA or RNA.
Prokaryotes generally reproduce by binary fission, a form of asexual reproduction. They are also known to use conjugation – which is often seen as the prokaryotic equivalent to sexual reproduction (however, it is NOT sexual reproduction).

Eukaryotic Cells

Main article : Eukaryotic Cells

Eukaryotic cells are characterised by a true nucleus.
The size of the cells ranges between 10–100 µm in diameter.
This broad category involves plants, fungi, protozoans, and animals.
The plasma membrane is responsible for monitoring the transport of nutrients and electrolytes in and out of the cells. It is also responsible for cell to cell communication.
They reproduce sexually as well as asexually.
There are some contrasting features between plant and animal cells. For eg., the plant cell contains chloroplast, central vacuoles, and other plastids, whereas the animal cells do not.

The cell structure comprises individual components with specific functions essential to carry out life’s processes. These components include- cell wall, cell membrane, cytoplasm, nucleus, and cell organelles. Read on to explore more insights on cell structure and function.

Cell Membrane

The cell membrane supports and protects the cell. It controls the movement of substances in and out of the cells. It separates the cell from the external environment. The cell membrane is present in all the cells.
The cell membrane is the outer covering of a cell within which all other organelles, such as the cytoplasm and nucleus, are enclosed. It is also referred to as the plasma membrane.
By structure, it is a porous membrane (with pores) which permits the movement of selective substances in and out of the cell. Besides this, the cell membrane also protects the cellular component from damage and leakage.
It forms the wall-like structure between two cells as well as between the cell and its surroundings.
Plants are immobile, so their cell structures are well-adapted to protect them from external factors. The cell wall helps to reinforce this function.
The cell wall is the most prominent part of the plant’s cell structure. It is made up of cellulose, hemicellulose and pectin.
The cell wall is present exclusively in plant cells. It protects the plasma membrane and other cellular components. The cell wall is also the outermost layer of plant cells.
It is a rigid and stiff structure surrounding the cell membrane.
It provides shape and support to the cells and protects them from mechanical shocks and injuries.
The cytoplasm is a thick, clear, jelly-like substance present inside the cell membrane.
Most of the chemical reactions within a cell take place in this cytoplasm.
The cell organelles such as endoplasmic reticulum, vacuoles, mitochondria, ribosomes, are suspended in this cytoplasm.
The nucleus contains the hereditary material of the cell, the DNA.
It sends signals to the cells to grow, mature, divide and die.
The nucleus is surrounded by the nuclear envelope that separates the DNA from the rest of the cell.
The nucleus protects the DNA and is an integral component of a plant’s cell structure.

Cell Organelles

Cells are composed of various cell organelles that perform certain specific functions to carry out life’s processes. The different cell organelles, along with its principal functions, are as follows:

Cell Theory was proposed by the German scientists, Theodor Schwann, Matthias Schleiden, and Rudolf Virchow. The cell theory states that:

All living species on Earth are composed of cells.
A cell is the basic unit of life.
All cells arise from pre-existing cells.

A modern version of the cell theory was eventually formulated, and it contains the following postulates:

Energy flows within the cells.
Genetic information is passed on from one cell to the other.
The chemical composition of all the cells is the same.

Functions of Cell

A cell performs major functions essential for the growth and development of an organism. Important functions of cell are as follows:

Provides Support and Structure

All the organisms are made up of cells. They form the structural basis of all the organisms. The cell wall and the cell membrane are the main components that function to provide support and structure to the organism. For eg., the skin is made up of a large number of cells. Xylem present in the vascular plants is made of cells that provide structural support to the plants.

Facilitate Growth Mitosis

In the process of mitosis, the parent cell divides into the daughter cells. Thus, the cells multiply and facilitate the growth in an organism.

Allows Transport of Substances

Various nutrients are imported by the cells to carry out various chemical processes going on inside the cells. The waste produced by the chemical processes is eliminated from the cells by active and passive transport. Small molecules such as oxygen, carbon dioxide, and ethanol diffuse across the cell membrane along the concentration gradient. This is known as passive transport. The larger molecules diffuse across the cell membrane through active transport where the cells require a lot of energy to transport the substances.

Energy Production

Cells require energy to carry out various chemical processes. This energy is produced by the cells through a process called photosynthesis in plants and respiration in animals.

Aids in Reproduction

A cell aids in reproduction through the processes called mitosis and meiosis. Mitosis is termed as the asexual reproduction where the parent cell divides to form daughter cells. Meiosis causes the daughter cells to be genetically different from the parent cells.

Thus, we can understand why cells are known as the structural and functional unit of life. This is because they are responsible for providing structure to the organisms and perform several functions necessary for carrying out life’s processes.

Also Read: Difference Between Plant Cell and Animal Cell

To know more about what is a cell, its definition, cell structure, types of cells, the discovery of cells, functions of cells or any other related topics, explore BYJU’S Biology . Alternatively, download BYJU’S app for a personalised learning experience.

Frequently Asked Questions

1. what is a cell, 2. state the characteristics of cells..

Cells provide the necessary structural support to an organism.
The genetic information necessary for reproduction is present within the nucleus.
Structurally, the cell has cell organelles which are suspended in the cytoplasm.
Mitochondria is the organelle responsible for fulfilling the cell’s energy requirements.
Lysosomes digest metabolic wastes and foreign particles in the cell.
Endoplasmic reticulum synthesises selective molecules and processes them, eventually directing them to their appropriate locations.

3. Highlight the cell structure and its components.

The cell structure comprises several individual components which perform specific functions essential to carry out life processes. The components of the cell are as follows:

Cell membrane
Nuclear membrane
Endoplasmic reticulum
Golgi Bodies
Mitochondria
Chloroplast

4. State the types of cells.

Cells are primarily classified into two types, namely

Prokaryotic cells
Eukaryotic cells

5. Elaborate Cell Theory.

Cell Theory was proposed by Matthias Schleiden, Theodor Schwann, and Rudolf Virchow, who were German scientists. The cell theory states that:

6. What is the function of mitochondria in the cells?

7. what are the functions of the cell.

The essential functions of the cell include:

The cell provides support and structure to the body.
It facilitates growth by mitosis.
It helps in reproduction.
Provides energy and allows the transport of substances.

8. What is the function of Golgi bodies?

9. who discovered the cell and how, 10. name the cell organelle that contains hydrolytic enzymes capable of breaking down organic matter., 11. which cellular structure regulates the entry and exit of molecules to and from the cell.

Register with BYJU'S & Download Free PDFs

Short Report
Open access
Published: 13 May 2024

Commonly used software tools produce conflicting and overly-optimistic AUPRC values

Wenyu Chen 1 na1 ,
Chen Miao 1 na1 ,
Zhenghao Zhang 2 ,
Cathy Sin-Hang Fung 1 ,
Ran Wang 1 ,
Yizhen Chen 2 ,
Yan Qian 3 ,
Lixin Cheng 4 ,
Kevin Y. Yip 2 , 5 ,
Stephen Kwok-Wing Tsui 1 , 6 &
Qin Cao ORCID: orcid.org/0000-0002-3106-562X 1 , 6 , 7

Genome Biology volume 25 , Article number: 118 ( 2024 ) Cite this article

197 Accesses

4 Altmetric

Metrics details

The precision-recall curve (PRC) and the area under the precision-recall curve (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluate 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in more than 3000 published studies. We find the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.

Introduction

Many problems in computational biology can be formulated as binary classification, in which the goal is to infer whether an entity (e.g., a cell) belongs to a target class (e.g., a cell type). Accuracy, precision, sensitivity (i.e., recall), specificity, and F1 score (Additional file 1 : Fig. S1) are some of the measures commonly used to quantify classification performance, but they all require a threshold of the classification score to assign every entity to either the target class or not. The receiver operating characteristic (ROC) and precision-recall curve (PRC) avoid this problem by considering multiple thresholds [ 1 ], which allows detailed examination of the trade-off between identifying entities of the target class and wrongly including entities not of this class. It is common to summarize these curves by the area under them (AUROC and AUPRC, respectively), which is a value between 0 and 1, with a larger value corresponding to better classification performance.

When the different classes have imbalanced sizes (e.g., the target cell type has few cells), AUPRC is a more sensitive measure than AUROC [ 1 , 2 , 3 , 4 ], especially when there are errors among the top predictions (Additional file 1 : Fig. S2). As a result, AUPRC has been used in a variety of applications, such as reconstructing biological networks [ 5 ], identifying cancer genes [ 6 ] and essential genes [ 7 ], determining protein binding sites [ 8 ], imputing sparse experimental data [ 9 ], and predicting patient treatment response [ 10 ]. AUPRC has also been extensively used as a performance measure in benchmarking studies, such as the ones for comparing methods for analyzing differential gene expression [ 11 ], identifying gene regulatory interactions [ 12 ], and inferring cell-cell communications [ 13 ] from single-cell RNA sequencing data.

Given the importance of PRC and AUPRC, we analyzed commonly used software tools and found that they produce contrasting results, some of which are overly-optimistic.

For each entity, a classifier outputs a score to indicate how likely it belongs to the target (i.e., “positive”) class. Depending on the classifier, the score can be discrete (e.g., random forest) or continuous (e.g., artificial neural network). Using a threshold t , the classification scores can be turned into binary predictions by considering all entities with a score $\ge t$ as belonging to the positive class and all other entities as not. When these predictions are compared to the actual classes of the entities, precision is defined as the proportion of entities predicted to be positive that are actually positive, while recall is defined as the proportion of actually positive entities that are predicted to be positive (Additional file 1 : Fig. S1).

The PRC is a curve that shows how precision changes with recall. In the most common way to produce the PRC, each unique classification score observed is used as a threshold to compute a pair of precision and recall values, which forms an anchor point on the PRC. Adjacent anchor points are then connected to produce the PRC.

When no two entities have the same score (Fig. 1 a), it is common to connect adjacent anchor points directly by a straight line [ 14 , 15 , 16 , 17 , 18 , 19 ] (Fig. 1 b). Another method uses an expectation formula, which we will explain below, to connect discrete points by piece-wise linear lines [ 20 ] (Fig. 1 c). The third method is to use the same expectation formula to produce a continuous curve between adjacent anchor points [ 17 , 21 ] (Fig. 1 d). A fourth method that has gained popularity, known as Average Precision (AP), connects adjacent anchor points by step curves [ 15 , 19 , 22 , 23 ] (Fig. 1 e). In all four cases, PRC estimates a function of precision in terms of recall based on the observed classification scores of the entities, and AUPRC estimates the integral of this function using trapezoids (in the direct straight line case), interpolation lines/curves (in the expectation cases), or rectangles (in the AP case).

Different methods for connecting adjacent anchor points on the PRC. a An illustrative data set with no two entities receiving the same classification score. b – e Different methods for connecting adjacent anchor points when there are no ties in classification scores, namely b direct straight line, c discrete expectation, d continuous expectation, and e AP. f An illustrative data set with different entities receiving the same classification score. Each group of entities with the same classification score defines a single anchor point (A, B, C, and D, from 3, 7, 2, and 1 entities, respectively). g – j Different methods for connecting anchor point B to its previous anchor point, A, namely g linear interpolation, h discrete expectation, i continuous expectation, and j AP. In c and h , tp is set to 0.5 and 1 in Formula 1, respectively (Additional file 1 : Supplementary Text)

When there are ties with multiple entities having the same score, which happens more easily with classifiers that produce discrete scores, these entities together define only one anchor point (Fig. 1 f). There are again four common methods for connecting such an anchor point to the previous anchor point, which correspond to the four methods for connecting anchor points when there are no ties (details in Additional file 1 : Supplementary Text). The first method is to connect the two anchor points by a straight line [ 15 , 18 , 19 ] (Fig. 1 g). This method is known to easily produce overly-optimistic AUPRC values [ 2 , 24 ], which we will explain below. The second method is to interpolate additional points between the two anchor points using a non-linear function and then connect the points by straight lines [ 14 , 17 , 20 ] (Fig. 1 h). The interpolated points appear at their expected coordinates under the assumption that all possible orders of the entities with the same score have equal probability. The third method uses the same interpolation formula as the second method but instead of creating a finite number of interpolated points, it connects the two anchor points by a continuous curve [ 17 , 21 ] (Fig. 1 i). Finally, the fourth method comes naturally from the AP approach, which uses step curves to connect the anchor points [ 15 , 19 , 22 , 23 ] (Fig. 1 j).

Using the four methods to connect anchor points when there are no ties and the four methods when there are ties can lead to very different AUPRC values (Fig. 1 , Additional file 1 : Fig. S3 and Supplementary Text).

Conceptual and implementation issues of some popular software tools

We analyzed 10 tools commonly used to produce PRC and AUPRC (Additional file 1 : Table S1). Based on citations and keywords, we estimated that these tools have been used in more than 3000 published studies in total ( Methods ).

The 10 tools use different methods to connect anchor points on the PRC and therefore they can produce different AUPRC values (Table 1 , Additional file 1 : Fig. S4–S7 and Supplementary Text). As a comparison, all 10 tools can also compute AUROC, and we found most of them to produce identical values (Additional file 1 : Supplementary Text).

We found five conceptual issues with some of these tools when computing AUPRC values (Table 1 ):

➀ Using the linear interpolation method to handle ties, which can produce overly-optimistic AUPRC values [ 2 , 24 ]. When interpolating between two anchor points, linear interpolation produces higher AUPRC than the other three methods under conditions that can easily happen in real situations (Additional file 1 : Supplementary Text)

➁ Always using (0, 1) as the starting point of the PRC (procedurally produced or conceptually derived, same for ➂ and ➄ below), which is inconsistent with the concepts behind the AP and non-linear expectation methods when the first anchor point with a non-zero recall does not have a precision of one (Additional file 1 : Supplementary Text)

➂ Not producing a complete PRC that covers the full range of recall values from zero to one

➃ Ordering entities with the same classification score by their order in the input and then handling them as if they have distinct classification scores

➄ Not putting all anchor points on the PRC

These issues can lead to overly-optimistic AUPRC values or change the order of two AUPRC values (Additional file 1 : Supplementary Text and Fig. S8-S13).

Some of these tools also produce a visualization of the PRC. We found three types of issues with these visualizations (Table 1 ):

Producing a visualization of PRC that has the same issue(s) as in the calculation of AUPRC

Producing a PRC visualization that does not always start the curve at a point with zero recall

Producing a PRC visualization that always starts at (0, 1)

Inconsistent AUPRC values and contrasting classifier ranks produced by the popular tools

To see how the use of different methods by the 10 tools and their other issues affect PRC analysis in practice, we applied them to evaluate classifiers in four realistic scenarios.

In the first scenario, we analyzed data from a COVID-19 study [ 25 ] in which patient blood samples were subjected to Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) assays [ 26 ]. We constructed a classifier for predicting CD4 $^+$ T cells, which groups the cells based on their transcriptome data alone and assigns a single cell type label to each group. Using cell type labels defined by the original authors as reference, which were obtained using both antibody-derived tags (ADTs) and transcriptome data, we computed the AUPRC of the classifier. Figure 2 a shows that the 10 tools produced 6 different AUPRC values, ranging from 0.416 to 0.684. In line with the conceptual discussions above, the AP method generally produced the smallest AUPRC values while the linear interpolation method generally produced the largest, although individual issues of the tools created additional variations of the AUPRC values computed.

The AUPRC values computed by the 10 tools in several realistic scenarios. a Predicting CD4 $^+$ T cells from single-cell transcriptomic data. b Predicting inflammatory bowel disease cases that belong to the ulcerative colitis subtype in the sbv IMPROVER Metagenomics Diagnosis for Inflammatory Bowel Disease Challenge. Only the top 8 submissions according to PRROC (discrete expectation) AUPRC values are included. c Predicting cases with preterm prelabor rupture of membranes in the DREAM Preterm Birth Prediction Challenge. In b and c , each entry shows the AUPRC value and the background color indicates its rank among the competitors

In the second scenario, we compared the performance of different classifiers that predict whether a patient has the ulcerative colitis (UC) subtype of inflammatory bowel disease (IBD) or does not have IBD, based on metagenomic data (processed taxonomy-based profile) [ 27 ]. The predictions made by these classifiers were submitted to the sbv IMPROVER Metagenomics Diagnosis for IBD Challenge. Their performance was determined by comparing against diagnosis of these patients based on clinical, endoscopic, and histological criteria. Figure 2 b shows that based on the AUPRC values computed, the 10 tools ranked the classifiers differently. For example, among the top 8 submissions with the highest performance, the classifier in submission 26 was ranked first in 8 cases, sole second place in 2 cases, and tied second place with another classifier in 4 cases (Fig. 2 b and Additional file 1 : Fig. S14). We observed similar rank flips when considering the top 30 submissions (Additional file 1 : Fig. S15 and S16).

In the third scenario, we compared the performance of different classifiers in identifying preterm prelabor rupture of the membranes (PPROM) cases from normal pregnancy in the DREAM Preterm Birth Prediction Challenge [ 28 ]. Based on the AUPRC values produced by the 10 tools, the 13 participating teams were ranked very differently (Fig. 2 c and Additional file 1 : Fig. S17). For example, Team “GZCDC” was ranked first (i.e., highest) in 3 cases, tenth in 4 cases, and thirteenth (i.e., lowest) in 7 cases. In addition to differences in the ranks, some of the AUPRC values themselves are also very different. For example, the AUPRC values computed by PerfMeas and MLeval have a Pearson correlation of − 0.759.

In the fourth scenario, we compared 29 classifiers that predicted target genes of transcription factors in the DREAM5 challenge [ 29 ]. Again, some classifiers received very different ranking based on the AUPRC values computed by the different tools (Additional file 1 : Fig. S18 and S19). For example, the classifier named “Other4” was ranked second based on the AUPRC values computed by PerfMeas but it was ranked twenty-fifth based on the AUPRC values computed by MLeval. In general, tools that use the discrete expectation, continuous expectation, and AP methods are in good agreements in this scenario, but they differ substantially from tools that use the linear interpolation method.

Conclusions

Due to their highly technical nature, it is easy to overlook the inconsistencies and issues of the software tools used for producing PRC and AUPRC. Some possible consequences include reporting overly-optimistic AUPRC, ranking classifiers differently by different tools, and introducing biases to the evaluation process, such as inflating the AUPRC of classifiers that produce discrete scores.

To address the problems, it is crucial to use tools that are free of the bugs described and avoid using the linear interpolation method (Table 1 ). It is also necessary to state clearly in manuscripts both the tool used (with its version number) and the underlying methods implemented by the tool for producing PRC and AUPRC. Whenever feasible, the adoption of multiple tools that implement different methods (e.g., one based on continuous expectation and one based on AP) is recommended, with comprehensive reporting of all their results.

Information about the tools

In this study, we included 12 tools commonly used for PRC and ROC analyses (Additional file 1 : Table S1). For each tool, we analyzed the latest stable version of it as of August 15, 2023. Because TorchEval had not released a stable version, we analyzed the latest version of it, version 0.0.6. Among the 12 tools, ten can compute both AUROC and AUPRC, while the remaining two can only compute AUROC. We focused on these 10 tools in the study of PRC and AUPRC. Some tools provide multiple methods for computing AUROC/AUPRC.

For tools with an associated publication, we obtained its citation count from Google Scholar. If a tool has multiple associated publications, we selected the one with the largest number of citations. As a result, the citation counts we report in Additional file 1 : Table S1 are underestimates if different publications associated with the same tool are not always cited together.

The Comprehensive R Archive Network (CRAN) packages PerfMeas and MLeval did not have an associated formal publication but only release notes. In each of these cases, we used the package name as keyword to search on Google Scholar and then manually checked the publications returned to determine the number of publications that cited these packages.

The CRAN package yardstick also did not have an associated formal publication. However, we were not able to use the same strategy as PerMeas and MLeval to determine the number of publications that cited the yardstick package since “yardstick” is an English word and the search returned too many publications to be verified manually. Therefore, we only counted the number of publications that cited yardstick’s release note, which is likely an underestimate of the number of publications that cited yardstick.

All citation counts were collected on October 9, 2023.

For tools with an associated formal publication, based on our collected lists of publications citing the tools, we further estimated the number of times the tools were actually used in the studies by performing keyword-based filtering. Specifically, if the main text or figure captions of a publication contains either one of the keywords “AUC” and “AUROC,” we assumed that the tool was used in that published study to perform ROC analysis. In the case of PRC, we performed filtering in two different ways and reported both sets of results in Additional file 1 : Table S1. In the first way, we assumed a tool was used in a published study if the main text or figure captions of the publication contains any one of the following keywords: “AUPR,” “AU-PR,” “AUPRC,” “AU-PRC,” “AUCPR,” “AUC-PR,” “PRAUC,” “PR-AUC,” “area under the precision recall,” and “area under precision recall.” In the second way, we assumed a tool was used in a published study if the main text or figure captions of the publication contains both “area under” and “precision recall.”

For the CRAN packages PerfMeas and MLeval, we estimated the number of published studies that actually used them by searching Google Scholar using the above three keyword sets each with the package name appended. We found that for all the publications we considered as using the packages in this way, they were also on our lists of publications that cite these packages. We used the same strategy to identify published studies that used the CRAN package yardstick. We found that some of these publications were not on our original list of publications that cite yardstick, and therefore we added them to the list and updated the citation count accordingly.

TorchEval was officially embedded into PyTorch in 2022. Due to its short history, among the publications that cite the PyTorch publication, we could not find any of them that used the TorchEval library.

Data collection and processing

We used four realistic scenarios to illustrate the issues of the AUPRC calculations.

In the first scenario, we downloaded CITE-seq data produced from COVID-19 patient blood samples by the COVID-19 Multi-Omic Blood ATlas (COMBAT) consortium [ 25 ]. We downloaded the data from Zenodo [ 30 ] and used the data in the “COMBAT-CITESeq-DATA” archive in this study. We then used a standard procedure to cluster the cells based on the transcriptome data and identified CD4 $^+$ T cells. Specifically, we extracted the raw count matrix of the transcriptome data and ADT features (“X” object) and the annotation data frame (“obs” object) from the H5AD file. We dropped all ADT features (features with names starting with “AB-”) and put the transcriptome data along with the annotation data frame into Seurat (version 4.1.1). We then log-normalized the transcriptome data (method “NormalizeData(),” default parameters), identified highly-variable genes (method “FindVariableFeatures(),” number of variable genes set to 10,000), scaled the data (method “ScaleData(),” default parameters), performed principal component analysis (method “RunPCA(),” number of principal components set to 50), constructed the shared/k-nearest neighbor (SNN/kNN) graph (method “FindNeighbours(),” default parameters), and performed Louvain clustering of the cells (method “FindClusters(),” default parameters). We then extracted the clustering labels generated and concatenated them with cell type, major subtype, and minor subtype annotations provided by the original authors, which were manually curated using both ADT and transcriptome information.

Our procedure produced 29 clusters, which contained 836,148 cells in total. To mimic a classifier that predicts CD4 $^+$ T cells using the transcriptome data alone, we selected one cluster and “predicted” all cells in it as CD4 $^+$ T cells and all cells in the other 28 clusters as not, based on which we computed an AUPRC value by comparing these “predictions” with the original authors’ annotations. We repeated this process for each of the 29 clusters in turn, and chose the one that gave the highest AUPRC as the final cluster of predicted CD4 $^+$ T cells.

For the second scenario, we obtained the data set used in the sbv IMPROVER (Systems Biology Verification combined with Industrial Methodology for PROcess VErification in Research) challenge on inflammatory bowel disease diagnosis based on metagenomics data [ 27 ]. The challenge involved 12 different tasks, and we focused on the task of identifying UC samples from non-IBD samples using the processed taxonomy-based profile as features. The data set contained 32 UC samples and 42 non-IBD samples, and therefore the baseline AUPRC was $\frac{32}{32+42} = 0.432$ . There were 60 submissions in total, which used a variety of classifiers. We obtained the classification scores in the submissions from Supplementary Information 4 of the original publication [ 27 ]. When we extracted the classification scores of each submission, we put the actual positive entities before the actual negative entities. This ordering did not affect the AUPRC calculations of most tools except those of PerfMeas, which depend on the input order of the entities with the same classification score.

To see how the different tools rank the top submissions, we first computed the AUPRC of each submission using PRROC (option that uses the discrete expectation method to handle ties) since we did not find any issues with its AUPRC calculations (Table 1 ). We then analyzed the AUPRC values produced by the 10 tools based on either the top 8 (Fig. 2 b and Additional file 1 : Fig. S14) or top 30 (Additional file 1 : Fig. S15 and S16) submissions.

For the third scenario, we downloaded the data set used in the Dialogue on Reverse Engineering Assessment and Methods (DREAM) Preterm Birth Prediction Challenge [ 28 ] from https://www.synapse.org/#!Synapse:syn22127152 . We collected the classification scores, from the object “prpile” in each team’s RData file, and the actual classes produced based on clinical evidence, from “anoSC2_v21_withkey.RData” ( https://www.synapse.org/#!Synapse:syn22127343 ). The challenge contained 7 scenarios, each of which had 2 binary classification tasks. For each scenario, 10 different partitioning of the data into training and testing sets were provided. We focused on the task of identifying PPROM cases from the controls under the D2 scenario defined by the challenge. For this task, the baseline AUPRC value averaged across the 10 testing sets was 0.386. There were 13 participating teams in total. For each team, we extracted its classification scores and placed the actual positive entities before the actual negative entities. For submissions that contained negative classification scores, we re-scaled all the scores to the range between 0 and 1 without changing their order since TensorFlow expects all classification scores to be between zero and one (Additional file 1 : Supplementary Text). Finally, for each team, we computed its AUPRC using each of the 10 testing sets and reported their average. We note that the results we obtained by using PRROC (option that uses the continuous expectation method to handle ties) were identical to those reported by the challenge organizer.

For the fourth scenario, we obtained the data set used in the DREAM5 challenge on reconstructing transcription factor-target networks based on gene expression data [ 29 ]. The challenge included multiple networks and we focused on the E. coli in silico Network 1, which has a structure that corresponds to the real E. coli transcriptional regulatory network [ 29 ]. We obtained the data from Supplementary Data of the original publication [ 29 ]. There were 29 submissions in total. For each submission, we extracted the classification scores of the predicted node pairs (each pair involves one potential transcription factor and one gene it potentially regulates) from Supplementary Data 3 and compared them with the actual classes (positive if the transcription factor actually regulates the gene; negative if not) in the gold-standard network from Supplementary Data 1. Both the submissions and the gold-standard were not required to include all node pairs. To handle this, we excluded all node pairs in a submission that were not included in the gold-standard (because we could not judge whether they are actual positives or actual negatives), and assigned a classification score of 0 to all node pairs in the gold-standard that were not included in a submission (because the submission did not give a classification score to them). The gold-standard contained 4012 interacting node pairs and 274,380 non-interacting node pairs, and therefore the baseline AUPRC value was $\frac{4012}{4012+274380} = 0.014$ .

Availability of data and materials

Our code is written in Java, Python and R. The reproducible code and all the data used in this paper are available at GitHub [ 31 ] and Zenodo [ 32 ].

Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE. 2015;10(3):e0118432.

Article PubMed PubMed Central Google Scholar

Davis J, Goadrich M. The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. New York: Association for Computing Machinery; 2006. p. 233–40.

He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21(9):1263–84.

Article Google Scholar

Lichtnwalter R, Chawla NV. Link prediction: fair and effective evaluation. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: IEEE; 2012. p. 376–83.

Cao Q, Anyansi C, Hu X, Xu L, Xiong L, Tang W, et al. Reconstruction of enhancer-target networks in 935 samples of human primary cells, tissues and cell lines. Nat Genet. 2017;49(10):1428–36.

Article CAS PubMed Google Scholar

Schulte-Sasse R, Budach S, Hnisz D, Marsico A. Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nat Mach Intell. 2021;3(6):513–26.

Hong C, Cao Q, Zhang Z, Tsui SKW, Yip KY. Reusability report: Capturing properties of biological objects and their relationships using graph neural networks. Nat Mach Intell. 2022;4(3):222–6.

Sielemann J, Wulf D, Schmidt R, Bräutigam A. Local DNA shape is a general principle of transcription factor binding specificity in Arabidopsis thaliana. Nat Commun. 2021;12(1):6549.

Article CAS PubMed PubMed Central Google Scholar

Li Z, Kuppe C, Ziegler S, Cheng M, Kabgani N, Menzel S, et al. Chromatin-accessibility estimation from single-cell ATAC-Seq data with scOpen. Nat Commun. 2021;12(1):6386.

Chowell D, Yoo SK, Valero C, Pastore A, Krishna C, Lee M, et al. Improved prediction of immune checkpoint blockade efficacy across multiple cancer types. Nat Biotechnol. 2022;40(4):499–506.

Dal Molin A, Baruzzo G, Di Camillo B. Single-cell RNA-sequencing: assessment of differential expression analysis methods. Front Genet. 2017;8:62.

Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali T. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat Methods. 2020;17(2):147–54.

Dimitrov D, Türei D, Garrido-Rodriguez M, Burmedi PL, Nagai JS, Boys C, et al. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nat Commun. 2022;13(1):3224.

Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics. 2005;21(20):3940–1.

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

Google Scholar

Valentini G, Re M. PerfMeas: performance measures for ranking and classification tasks. R package. 2014. https://cran.r-project.org/web/packages/PerfMeas .

Grau J, Grosse I, Keilwagen J. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics. 2015;31(15):2595–7.

John C. MLeval: machine learning model evaluation. R package. 2020. https://cran.r-project.org/web/packages/MLeval .

Kuhn M, Vaughan D. yardstick: tidy characterizations of model performance. R package. 2021. https://cran.r-project.org/web/packages/yardstick .

Saito T, Rehmsmeier M. Precrec: fast and accurate precision-recall and ROC curve calculations in R. Bioinformatics. 2017;33(1):145–7.

Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:160304467. 2016. https://arxiv.org/abs/1603.04467 .

Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGKDD Explor Newsl. 2009;11(1):10–8.

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. PyTorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst. 2019;32:8026–37.

Flach P, Kull M. Precision-recall-gain curves: PR analysis done right. Adv Neural Inf Process Syst. 2015;28:838–46.

COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium. A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. Cell. 2022;185(5):916–938.e58.

Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14(9):865–8.

Khachatryan L, Xiang Y, Ivanov A, Glaab E, Graham G, Granata I, et al. Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge. Sci Rep. 2023;13:6303.

Tarca AL, Pataki BÁ, Romero R, Sirota M, Guan Y, Kutum R, et al. Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth. Cell Rep Med. 2021;2(6):100323.

Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, et al. Wisdom of crowds for robust gene network inference. Nat Methods. 2012;9(8):796–804.

COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium. A blood atlas of COVID-19 defines hallmarks of disease severity and specificity: Associated data. Zenodo. 2022. https://doi.org/10.5281/zenodo.6120249 .

Chen W, Miao C, Zhang Z, Fung CSH, Wang R, Chen Y, et al. Commonly used software tools produce conflicting and overly-optimistic AUPRC values. GitHub. 2024. https://github.com/wychencuhk/AUPRC_project .

Chen W, Miao C, Zhang Z, Fung CSH, Wang R, Chen Y, et al. Commonly used software tools produce conflicting and overly-optimistic AUPRC values. Zenodo. 2024. https://doi.org/10.5281/zenodo.11076192 .

Download references

Review history

The review history is available as Additional file 2 .

Peer review information

Andrew Cosgrove was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

QC is supported by National Natural Science Foundation of China under Award Number 32100515. YQ is supported by Guangdong Basic and Applied Basic Research Foundation under Award Number 2021A1515110759 and Natural Science Foundation of Guangdong Province of China under Award Number 2023A1515011187. LC is supported by National Natural Science Foundation of China under Award Number 32370711 and Shenzhen Medical Research Fund under Award Number A2303033. KYY is supported by National Cancer Institute of the National Institutes of Health under Award Number P30CA030199, National Institute on Aging of the National Institutes of Health under Award Numbers U54AG079758 and R01AG085498, and internal grants of Sanford Burnham Prebys Medical Discovery Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Wenyu Chen and Chen Miao are co-first authors.

Authors and Affiliations

School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China

Wenyu Chen, Chen Miao, Cathy Sin-Hang Fung, Ran Wang, Stephen Kwok-Wing Tsui & Qin Cao

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China

Zhenghao Zhang, Yizhen Chen & Kevin Y. Yip

The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medical College of Jinan University, Shenzhen, China

Lixin Cheng

Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA

Kevin Y. Yip

Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China

Stephen Kwok-Wing Tsui & Qin Cao

Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China

You can also search for this author in PubMed Google Scholar

Contributions

KYY, SKWT, and QC conceived and supervised the project. WC, CM, KYY, and QC designed the computational experiments and data analyses. WC, CM, and YC surveyed and collected the tools. WC, CM, ZZ, CSHF, and RW prepared the data. WC and CM conducted the computational experiments. WC, CM, ZZ, and RW performed the data analyses. All the authors interpreted the results. WC, CM, KYY, and QC wrote the manuscript.

Corresponding authors

Correspondence to Kevin Y. Yip , Stephen Kwok-Wing Tsui or Qin Cao .

Ethics declarations

Ethics approval and consent to participate.

No ethical approval was required for this study. All utilized public datasets were generated by other organizations that obtained ethical approval.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1. supplementary text, figures and tables., additional file 2. review history., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Chen, W., Miao, C., Zhang, Z. et al. Commonly used software tools produce conflicting and overly-optimistic AUPRC values. Genome Biol 25 , 118 (2024). https://doi.org/10.1186/s13059-024-03266-y

Download citation

Received : 07 February 2024

Accepted : 30 April 2024

Published : 13 May 2024

DOI : https://doi.org/10.1186/s13059-024-03266-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Genome Biology

ISSN: 1474-760X

Submission enquiries: [email protected]
General enquiries: [email protected]

'Science has a wide community and you can always find people to help'

5/17/2024 A&S Communications

Shiva Dahagam

Chemistry, Biology, Economics Berwyn, PA

Why did you choose Cornell?

Cornell made me feel like I could be at the forefront of whatever field I wanted to explore. Coming to Cornell, I knew I wanted to get involved in research, volunteering and sports and this place made me feel like I could do it all and do it in the best way possible.

What was your favorite class and why?

ECON 3130: Statistics and Probability. I had Professor Doug McKee for four consecutive semesters at Cornell, but this class was by far one of my favorites because of the time we took to understand the nuances and logic behind probability. I think economics and chemistry rely heavily on statistical theory to explain how their fields work because we can’t “see” every single molecule in a reaction or every single individual in a population. This class taught me to appreciate the math behind how we treat information that is too vast to understand and made me a better student of chemistry and economics because of it.

What are the most valuable skills you gained from your Arts & Sciences education?

Being brave in uncertain situations. I think a lot of Cornell students come from strong academic backgrounds where there is always a surefire way of achieving success. The real world is not so straightforward and sometimes you have to be willing to invest time and energy into a project, extracurricular or activity not knowing whether or not it will be successful. I think the second part of this skill is accepting unexpected/undesired outcomes and having the self-confidence to learn and move forward. There is no such thing as a failed experiment if you can learn something from the results.

What have you accomplished as a Cornell student that you are most proud of?

I am most proud of the work I was able to accomplish in research that ultimately led to a publication. I think research work requires you to think outside the box and solve problems in a different way than you would in a class. It’s harder to solve problems that have not been solved before, but it forces you to learn and develop new ways of finding and synthesizing pieces of information together. More importantly, I think it taught me the importance of perseverance and asking for help when you need it. Science has a wide community and you can always find people to help you are brave enough to ask.

Who or what influenced your Cornell education the most?

My research mentors, Dr. Todd Hyster and Dr. Nozomi Ando, definitely influenced me the most. They took me under their wings and showed me how fun and exciting research could be. They are forever my role models in how to dig deep into your passions and find new ways to use that knowledge to benefit the world around us.

Chemistry Senior Recognized as 2024 Merrill Presidential Scholar

'Each subset of chemistry is fascinating and intriguing in its own way'

Dead & Company concert funds $800K for new climate solutions

Four Cornell professors elected to national academy

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

Machine learning
Social justice
Black holes
Classes and programs

Departments

Aeronautics and Astronautics
Brain and Cognitive Sciences
Architecture
Political Science
Mechanical Engineering

Centers, Labs, & Programs

Abdul Latif Jameel Poverty Action Lab (J-PAL)
Picower Institute for Learning and Memory
Lincoln Laboratory
School of Architecture + Planning
School of Engineering
School of Humanities, Arts, and Social Sciences
Sloan School of Management
School of Science
MIT Schwarzman College of Computing

The beauty of biology

Press contact :.

Previous image Next image

When Hanjun Lee arrived at MIT, he was set on becoming a Course 5 chemistry student. Based on his experience in high school, biology was all about rote memorization.

That changed when he took course 7.03 (Genetics) , taught by then-professor Aviv Regev , now head and executive vice president of research and early development at Genentech, and Peter Reddien , professor of biology and core member and associate director of the Whitehead Institute for Biomedical Research.

He notes that friends from other schools don’t cite a single course that changed their major, but he’s not alone in choosing Course 7 because of 7.03.

“Genetics has this interesting force, especially in MIT biology. The department’s historical — and active — role in genetics research ties directly into the way the course is taught,” Lee says. “Biology is about logic, scientific reasoning, and posing the right questions.”

A few years later, as a teaching assistant for class 7.002 (Fundamentals of Experimental Molecular Biology ), he came to value how much care MIT biology professors take in presenting the material for all offered courses.

“I really appreciate how much effort MIT professors put into their teaching,” Lee says. “As a TA, you realize the beauty of how the professors organize these things — because they’re teaching you in a specific way, and you can grasp the beauty of it — there’s a beauty in studying and finding the patterns in nature.”

An undertaking to apply

To attend MIT at all hadn’t exactly been a lifelong dream. In fact, it didn’t occur to Lee that he could or should apply until he represented South Korea at the 49th International Chemistry Olympiad, where he won a Gold Medal in 2017. There, he had the chance to speak with MIT alumni, as well as current and aspiring students. More than half of those aspiring students eventually enrolled, Lee among them.

“Before that, MIT was this nearly mythical institution, so that experience really changed my life,” Lee recalls. “I heard so many different stories from people with so many different backgrounds — all converging towards the same enthusiasm towards science.”

At the time, Lee was already attending medical school — a six-year undergraduate program in Korea — that would lead to a stable career in medicine. Attending MIT would involve both changing his career plans and uprooting his life, leaving all his friends and family behind.

His parents weren’t especially enthusiastic about his desire to study at MIT, so it was up to Lee to meet the application requirements. He woke up at 3 a.m. to find his own way to the only SAT testing site in South Korea — an undertaking he now recalls with a laugh. In just three months, he had gathered everything he needed; MIT was the only institution in the United States Lee applied to.

He arrived in Cambridge, Massachusetts, in 2018 but attended MIT only for a semester before returning to Korea for his two years of mandatory military service.

“During military service, my goal was to read as many papers as possible, because I wondered what topic of science I’m drawn to — and many of the papers I was reading were authored by people I recognized, people who taught biology at MIT,” Lee says. “I became really interested in cancer biology.”

Return to MIT

When he returned to campus, Lee pledged to do everything he could to meet with faculty and discuss their work. To that end, he joined the MIT Undergraduate Research Journal , allowing him to interview professors. He notes that most MIT faculty are enthusiastic about being contacted by undergraduate students.

Stateside, Lee also reached out to Michael Lawrence , an assistant professor of pathology at Harvard Medical School and assistant geneticist at Mass General Cancer Center, about a preprint concerning APOBEC, an enzyme Lee had studied at Seoul National University. Lawrence’s lab was looking into APOBEC and cancer evolution — and the idea that the enzyme might drive drug resistance to cancer treatment.

“Since he joined my lab, I’ve been absolutely amazed by his scientific talents,” Lawrence says. “Hanjun’s scientific maturity and achievements are extremely rare, especially in an undergraduate student.”

Lee has made new discoveries from genomic data and was involved in publishing a paper in Molecular Cell and a paper in Nature Genetics . In the latter, the lab identified the source of background noise in chromosome conformation capture experiments, a technique for analyzing chromatin in cells.

Lawrence thinks Lee “is destined for great leadership in science.” In the meantime, Lee has gained valuable insights into how much work these types of achievements require.

“Doing research has been rewarding, but it also taught me to appreciate that science is almost 100 percent about failures,” Lee says. “It is those failures that end up leading you to the path of success.”

Widening the scope

Lee’s personal motto is that to excel in a specific field, one must have a broad sense of what the entire field looks like, and suggests other budding scientists enroll in courses distant from their research area. He also says it was key to see his peers as collaborators rather than competitors, and that each student will excel in their own unique way.

“Your MIT experience is defined by interactions with others,” Lee says. “They will help identify and shape your path.”

For his accomplishments, Lee was recently named an American Association for Cancer Research Undergraduate Scholar . Last year, he also spoke at the Gordon Research Conference on Cell Growth and Proliferation about his work on the retinoblastoma gene product RB.

Encouraged by positive course evaluations during his time as a TA, Lee hopes to inspire other students in the future through teaching. Lee has recently decided to pursue a PhD in cancer biology at Harvard Medical School, although his interests remain broad.

“I want to explore other fields of biology as well,” he says. “I have so many questions that I want to answer.”

Although initially resistant, Lee’s mother and father are now “immensely proud to be MIT parents” and will be coming to Cambridge in May to celebrate Lee’s graduation.

“Throughout my years here, they’ve been able to see how I’ve changed,” he says. “I don’t think I’m a great scientist, yet, but I now have some sense of how to become one.”

Share this news article on:

More MIT News

Colorful rendering shows a lattice of black and grey balls making a honeycomb-shaped molecule, the MOF. Snaking around it is the polymer, represented as a translucent string of teal balls. Brown molecules, representing toxic gas, also float around.

Researchers develop a detector for continuously monitoring toxic gases

Read full story →

Navigating longevity with industry leaders at MIT AgeLab PLAN Forum

Jeong Min Park poses leaning on an outdoor sculpture in Killian Court.

Jeong Min Park earns 2024 Schmidt Science Fellowship

A cute robot is at the chalkboard. The chalkboard is filled with complex charts, waves and shapes.

Scientists use generative AI to answer complex questions in physics

A hand holds a phone with an image displayed on the screen. A word bubble says “Accurate?” and a big green check mark is on the content. The background has blurry boxes of social media websites.

New tool empowers users to fight online misinformation

Elaine Liu leans against an electric vehicle charger inside a parking garage.

Elaine Liu: Charging ahead

More news on MIT News homepage →

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

Map (opens in new window)
Events (opens in new window)
People (opens in new window)
Careers (opens in new window)
Accessibility
Social Media Hub
MIT on Facebook
MIT on YouTube
MIT on Instagram

COMMENTS

Biological classification
Classification of fungi Get 3 of 4 questions to level up! Plant kingdom. Learn. Plant Kingdom (Opens a modal) Alternation of Generations (Opens a modal) Bryophytes (Opens a modal) Pteridophytes (Opens a modal) Gymnosperms - the male cone (Opens a modal) Reproduction in gymnosperms
Practice Worksheet on Taxonomy and Classification
Taxonomy is often introduced with evolution, where students learn how to analyze phylogenetic trees and create cladograms. This worksheet is a simple reinforcement exercise that covers the six kingdoms and the classification system developed by Carolus Linnaeus. I teach my students to learn this system with the mnemonic " K ing P hilip C ame ...
PDF Unit 1 Characteristics and classiﬁcation of living organisms
1In your notebook, write two reasons why living organisms are classiﬁed into groups. 2Use your knowledge about classiﬁcation system to classify a lion into kingdom, phylum, class, order, family, genus and species. 3Look at the following three scientiﬁc names: (i)Merluccius Capensis. (ii)homo sapiens.
4.3: Classification and Identification
Figure 4.3.2 4.3. 2: Ernst Haeckel's rendering of the tree of life, from his 1866 book General Morphology of Organisms, contained three kingdoms: Plantae, Protista, and Animalia. He later added a fourth kingdom, Monera, for unicellular organisms lacking a nucleus. Nearly 100 years later, in 1969, American ecologist Robert Whittaker (1920 ...
10.2: Animal Classification
Invertebrates are animals that lack a vertebral column, or backbone. The last phylum in the table, the Chordata, also includes many invertebrate species. Tunicates and lancelets are both invertebrates. Altogether, invertebrates make up at least 95 percent of all animal species. The remaining animals are vertebrates.
The Taxonomic Classification System
For the common dog, the classification levels would be as shown in Figure 1. Therefore, the full name of an organism technically has eight terms. For the dog, it is: Eukarya, Animalia, Chordata, Mammalia, Carnivora, Canidae, Canis, and lupus. Notice that each name is capitalized except for species, and the genus and species names are italicized.
Taxonomy
Taxonomy Definition. Taxonomy is the branch of biology that classifies all living things. It was developed by the Swedish botanist Carolus Linnaeus, who lived during the 18 th Century, and his system of classification is still used today. Linnaeus invented binomial nomenclature, the system of giving each type of organism a genus and species name.
Taxonomy
Scientific classification is a method by which biologists organize living things into groups. It is also called taxonomy. Groups of organisms in taxonomy are called taxa (singular, taxon ). You may already be familiar with commonly used taxa, such as the kingdom and species. A kingdom is a major grouping of organisms, such as plants or animals.
Taxonomy (biology)
In biology, taxonomy (from Ancient Greek τάξις () 'arrangement', and -νομία () 'method') is the scientific study of naming, defining (circumscribing) and classifying groups of biological organisms based on shared characteristics. Organisms are grouped into taxa (singular: taxon) and these groups are given a taxonomic rank; groups of a given rank can be aggregated to form a more ...
Biology library
Biology is the study of life. Here, you can browse videos, articles, and exercises by topic. We keep the library up-to-date, so you may find new or improved content here over time. ... Our mission is to provide a free, world-class education to anyone, anywhere. Khan Academy is a 501(c)(3) nonprofit organization. Donate or volunteer today! Site ...
1.7: Assignment- Visualizing Taxonomy
The assignment on this page is aligned to a learning outcome of Biology for Non-Majors I and we've identified the module where the reading appears. The assignment can be created with basic web and computing tools, a cell phone camera or any video recording device, Google or Word documents, and your learning management system.
Taxonomy
taxonomy, in a broad sense the science of classification, but more strictly the classification of living and extinct organisms—i.e., biological classification. The term is derived from the Greek taxis ("arrangement") and nomos ("law").Taxonomy is, therefore, the methodology and principles of systematic botany and zoology and sets up arrangements of the kinds of plants and animals in ...
Species (video)
Transcript. Taxonomy comes to life through the exploration of species, defined as groups of animals that can interbreed to produce fertile offspring. Examples of species include lions, tigers, and horses. Hybrids are the offspring of different species, which are usually infertile. Examples of hybrids include mules and ligers. Created by Sal Khan.
Methods of Classification Flashcards
A. DNA sequencing. B. fossil record. C. discovery of new species. D. physical characteristics. What is a binomial nomenclature? A. a two word system for naming organisms by using the genus and species. B. a two word system for naming organisms by using the family and genus. C. a two word system for naming organisms by using the order and family.
Classification of Animals
The members of the animal kingdom are subdivided into more than 30 different groups called phyla. There are 7 major hierarchies, or ranks, in animal classification. Kingdom is the highest and most inclusive rank. Phylum is the rank below kingdom, and species is the most fundamental rank. The hierarchy of the ranks is shown in Figure below.
METHODS OF CLASSIFICATION ASSIGNMENT Flashcards
Study with Quizlet and memorize flashcards containing terms like In this assignment you will practice what you have learned about the methods of classification. Specifically, you will answer questions about how organisms are classified and explain why systems of classification may change., Fill in the blank below with the vocabulary word that best completes the sentence. _________ is the ...
Biology 6.01: Classification of Living Organisms
Biology (6.01) Classification of Living Organisms By Jonah G For this assignment I have chosen the "Bird Group" which consisted of a... Blue Jay Robin Cardinal Finch Pelican Kingdom Animalia Chordata Phylum Aves Class Passeriformes Pelecaniformes Order Family Corvidae Turdidae
Assignments
Introduction to Biology. Menu. More Info Syllabus Calendar Readings Recitations Assignments Exams Study Materials Video Lectures Assignments. assignments ... assignment_turned_in Problem Sets with Solutions. grading Exams with Solutions. notes Lecture Notes. Download Course.
Hands-on Activities for Teaching Biology to High School and Middle
cellular respiration and photosynthesis. cell division and genetics. molecular biology. evolution and ecology. human physiology. The expression "hands-on, minds-on" summarizes the philosophy we have incorporated in these activities - namely, that students will learn best if they are actively engaged and if their activities are closely linked to ...
Biology Worksheets, Notes, and Quizzes (PDF and PNG)
This is a collection of free biology worksheets, notes, handouts, slides, study guides and quizzes. Most content targets high school, AP biology, genetics, anatomy/physiology, immunology, and biology 101 and 102 in college. There is also biochemistry and physics for biologists. However, some resources are at the grade school and middle school ...
Assignments
Module Alignment. Biological Astronaut (download the .docx, .rtf) After both Module 1: Introduction to Biology and Module 2: Chemistry of Life. Nutritionist for a Day (download the .docx, .rtf) After Module 3: Important Biological Macromolecules. Cell Builder (download the .docx, .rtf) After Module 4: Cellular Structure.
High school biology
New high school biology course coming soon! In July 2024, we're replacing this course with a brand new high school biology course aligned to NGSS! Get ready to explore the science of life, from cells to ecosystems, through all new videos, articles, and exercises. Get more info here.
What Is A Cell?
By structure, it is a porous membrane (with pores) which permits the movement of selective substances in and out of the cell. Besides this, the cell membrane also protects the cellular component from damage and leakage. It forms the wall-like structure between two cells as well as between the cell and its surroundings.
Commonly used software tools produce conflicting and overly-optimistic
The precision-recall curve (PRC) and the area under the precision-recall curve (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluate 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in more than 3000 published studies. We find ...
'Science has a wide community and you can always find people to help'
Chemistry, Biology, Economics Berwyn, PA. Why did you choose Cornell? Cornell made me feel like I could be at the forefront of whatever field I wanted to explore. Coming to Cornell, I knew I wanted to get involved in research, volunteering and sports and this place made me feel like I could do it all and do it in the best way possible.
The beauty of biology
The beauty of biology. Senior Hanjun Lee planned to pursue chemistry at MIT. A course in genetics changed that. Hanjun Lee will pursue a PhD in cancer biology at Harvard Medical School this fall. Photo courtesy of Hanjun Lee. An illustration for the paper "Chromatin-bound RB targets promoters, enhancers, and CTCF-bound loci and is ...

The Biology Corner

Practice with Taxonomy

Margin Size

1.7: Assignment- Visualizing Taxonomy

Learning Objectives

Browse Course Material

Departments

You are leaving MIT OpenCourseWare

A Random Walk

Hands-on Activities for Teaching Biology to High School and Middle School Students

Science and PBL

Biology. AP Biology, Forensic, Anatomy

Using our material in an online lab manual

Post new comment

Intro and Biological Molecules

Cell Structure and Function

Cellular Respiration and Photosynthesis

Cell Division

Molecular Biology

Human Physiology and Health

Creative Commons License

Remote Ready Biology Learning Activities

What's New? Subscribe to Serendip Studio

Biology Worksheets, Notes, and Quizzes (PDF and PNG)

Biochemistry

General and Cell Biology

Anatomy and Physiology

Parasitology

Physical Science for Biology

Biology Labs

Biology Word Search Puzzles

Miscellaneous Biology Notes and Resouces

Biology Notes Terms of Use

Related Posts

Faculty Resources

Cell Definition

Discovery of Cells

Who discovered cells?

Prokaryotic Cells

Eukaryotic Cells

Cell Membrane

Cell Organelles

Functions of Cell

Provides Support and Structure

Facilitate Growth Mitosis

Allows Transport of Substances

Energy Production

Aids in Reproduction

Frequently Asked Questions

3. Highlight the cell structure and its components.

4. State the types of cells.

5. Elaborate Cell Theory.

6. What is the function of mitochondria in the cells?

8. What is the function of Golgi bodies?

Leave a Comment Cancel reply

Register with BYJU'S & Download Free PDFs

Commonly used software tools produce conflicting and overly-optimistic AUPRC values

Introduction

Conceptual and implementation issues of some popular software tools

Inconsistent AUPRC values and contrasting classifier ranks produced by the popular tools

Conclusions

Information about the tools

Data collection and processing

Availability of data and materials

Review history

Peer review information

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Competing interests

Additional information

Supplementary Information

About this article

Share this article

Genome Biology

'Science has a wide community and you can always find people to help'

Shiva Dahagam

Why did you choose Cornell?