Bioinformatics Review
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Current Research Topics in Bioinformatics
Researchers working in the scientific area always want to explore new and hot topics to make informed choices. In this article, all new, current, and demanding research topics in bioinformatics are mentioned. This article is helpful for the researchers who are looking for trends in bioinformatics to select a research topic of broad-spectrum.
Since the research in bioinformatics and its applications
are exponentially increasing every year, it is essential to know hot topics for researchers who are trying to make a career in this area. Currently, most of the research is focused on treating deadly diseases such as “ cancer, coronary artery disease, HIV, chronic infections ”, and so on . In silico drug designing is always demanding in designing inhibitors or potential drugs for such diseases. Besides, a lot of scientists are working on next-generation sequencing, big data , and cancer . A recent study has found that the interest of researchers in these topics plateaued over after the early 2000s [1].
Besides the above mentioned hot topics, the following topics are considered demanding in bioinformatics.
- Cloud computing, big data, Hadoop
- Machine learning
- Artificial intelligence
- Functional genomics
- Rna-seq analysis (equally relevant along with next-generation sequencing techniques)
- Data mining (including text search, data integration, database development, and management)
- Neural networks
- Mathematical modeling
- Mirna function identification
- Evolutionary studies
- Genomics, transcriptomics, and proteomics
- Metabolomics
If you are new and trying to learn bioinformatics, then read the following articles:
- Bioinformatics- Where & How to Start?
- List of Bioinformatics Books for Beginners
- Hahn A., Mohanty S.D., Manda P. (2017) What’s Hot and What’s Not? – Exploring Trends in Bioinformatics Literature Using Topic Modeling and Keyword Analysis. In: Cai Z., Daescu O., Li M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science, vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_25
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5 Machine Learning Projects in Bioinformatics For Practice
Explore Top Machine Learning Projects Ideas to Understand the Applications of Machine Learning in Bioinformatics| ProjectPro
The term "bioinformatics" represents the use of computation and analysis methods to collect and analyze biological data. It's a multidisciplinary field that combines genetics, biology, statistics, mathematics, and computer science. Various branches of bioinformatics, including genomics, proteomics, and microarrays, extensively use machine learning for better outcomes.
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Top 5 Machine Learning Projects in Bioinformatics
Here are five exciting machine learning projects for bioinformatics to help you understand the application of machine learning in healthcare , mainly bioinformatics.
1. Anti-Cancer Drug Efficacy Prediction
Predicting which patients are likely to benefit or not from a specific therapy is a significant concern in cancer treatment because, generally speaking, not all patients will benefit from a particular medication. This enhances the efficacy of treatment and reduces the suffering and misery experienced by non-responders. Thus, there is an immediate need to find reliable biomarkers (i.e., genes or proteins) that can precisely predict which patients respond best to which medications. For this project, you will use fundamental data science techniques , such as data processing, integration, analysis, and visualization, to determine the most effective biomarkers for various cancer types.
2. Autism Mutation Detection
In this machine learning project for bioinformatics, you will develop a deep-learning-based system that predicts the accurate regulatory effects and the harmful impacts of genetic variants to address the issue of detecting the impact of noncoding mutations on disease. This predictive genomics framework is likely relevant to complex human diseases, illustrates the significance of noncoding mutations in ASD [autism spectrum disorder], and identifies mutations with higher effects for further analysis. If you want to add some unique project to your machine learning portfolio , you must try working on this project.
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3. Personalized Cancer Medication
This deep learning project can predict how different genetic variations affect a patient's health. You can use the MSKCC (Memorial Sloan Kettering Cancer Center) database, including thousands of mutations that top-notch scientists and physicians have thoroughly classified. For this machine learning project, you will create a machine learning algorithm using the Keras deep learning library and LSTM that automatically categorizes genetic variants utilizing this data set as a starting point. Additionally, this project entails using various NLP text processing techniques such as Lemmatization, Stemming, Tokenization, etc.
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4. Human Disease Genetic Basis Identification
Human genomes vary between individuals by.1%. Our genetic inclination to specific disorders, such as hypertension, is encoded within this small degree of variation. We can accurately define which gene variants belong to each disease by comparing populations of healthy and diseased people and their variations in the genes responsible for the diseases. In this bioinformatics, AI and machine learning project, strategies for finding the variation corresponding to disease are developed, along with statistics to support the predictions. Furthermore, this project develops methods for predicting how a gene mutation can alter the structure of the protein or the regulatory structure. You can also estimate the disease risk factor's history and evolution by recreating the genes' phylogeny.
5. Build a DNA Sequence Classifier
You will use a classification model in this project that can predict a gene's function just from the DNA sequence of the coding sequence. You will create a function that will extract from any sequence string all overlapping k-mers of a given length, count the k-mers and convert the k-mers list for each gene into string sequences using scikit-learn NLP tools.
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