research paper using machine learning

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Machine learning articles from across Nature Portfolio

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.

SCUBA-D: a freshly trained diffusion model generates high-quality protein structures

The accuracy of SCUBA-D, a protein backbone structure diffusion model trained independently and orthogonally to existing protein structure prediction networks, is confirmed by the X-ray structures of 16 designed proteins and a protein complex, and by experimental validation of designed heme-binding proteins and Ras-binding proteins.

AI-designed DNA sequences regulate cell-type-specific gene expression

Researchers have used artificial-intelligence models to create regulatory DNA sequences that drive gene expression in specific cell types. Such synthetic sequences could be used to target gene therapies to particular cell populations.

Andreas R. Pfenning

Designed with interactome-based deep learning

Predicting prospective drug-like molecules quickly and accurately is a considerable challenge for de novo drug design. An interactome-based deep learning method has been developed that outperforms standard chemical language models.

Xueying Mao
Dongqing Wei

Latest Research and Reviews

Efficient generation of protein pockets with PocketGen

A generative model that leverages a graph transformer and protein language model to generate residue sequences and full-atom structures of protein pockets is introduced, which outperforms state-of-the-art approaches.

Zaixi Zhang
Wan Xiang Shen
Marinka Zitnik

scPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases

Multimodal single-cell analysis faces challenges due to high feature dimensionality and shallow sequencing depth. Here, authors present scPair for aligning cell states across modalities with implicit feature selection and enhancing data analysis tasks such as identifying key transcription factors in neural differentiation.

Gerald Quon

Digital profiling of gene expression from histology images with linearized attention

Predicting gene alterations and expression from whole-slide images (WSIs) can be a cost-efficient solution for cancer profiling. Here, the authors develop SEQUOIA, a transformer model with linearised attention to predict gene expression from WSIs, and validate its performance and clinical utility across multiple pan-cancer datasets.

Marija Pizurica
Yuanning Zheng
Olivier Gevaert

Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing

This Resource uses deep learning-based tools to build a dynamic transcriptomic reference for mouse and human preimplantation development.

Martin Proks
Nazmus Salehin
Joshua M. Brickman

Using data from cue presentations results in grossly overestimating semantic BCI performance

Milan Rybář
Riccardo Poli

A multimodal fusion network based on a cross-attention mechanism for the classification of Parkinsonian tremor and essential tremor

Qianyuan Hu
Chengli Song

News and Comment

Can robotic lab assistants speed up your work?

When it comes to laboratory automation, small and simple is the winning combination.

Carrie Arnold

Why AI-generated recommendation letters sell applicants short

ChatGPT can do many things, but writing a personal endorsement is not one of them, says Maroun Khoury.

Maroun Khoury

Geothermal power is vying to be a major player in the world’s clean-energy future

With technical advances and enthusiasm from policymakers, advocates say the time for next-generation geothermal has come.

Davide Castelvecchi

Can AI review the scientific literature — and figure out what it all means?

Artificial intelligence could help speedily summarize research. But it comes with risks.

Helen Pearson

AI protein-prediction tool AlphaFold3 is now more open

The code underlying the Nobel-prize-winning tool for modelling protein structures can now be downloaded by academics.

Ewen Callaway

ChatGPT is transforming peer review — how can we use it responsibly?

At major computer-science publication venues, up to 17% of the peer reviews are now written by artificial intelligence. We need guidelines before things get out of hand.

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An explainable machine learning model for identifying geographical origins of sea cucumber Apostichopus japonicus based on multi-element profile

A comparison of machine learning- and regression-based models for predicting ductility ratio of rc beam-column joints, alexa, is this a historical record.

Digital transformation in government has brought an increase in the scale, variety, and complexity of records and greater levels of disorganised data. Current practices for selecting records for transfer to The National Archives (TNA) were developed to deal with paper records and are struggling to deal with this shift. This article examines the background to the problem and outlines a project that TNA undertook to research the feasibility of using commercially available artificial intelligence tools to aid selection. The project AI for Selection evaluated a range of commercial solutions varying from off-the-shelf products to cloud-hosted machine learning platforms, as well as a benchmarking tool developed in-house. Suitability of tools depended on several factors, including requirements and skills of transferring bodies as well as the tools’ usability and configurability. This article also explores questions around trust and explainability of decisions made when using AI for sensitive tasks such as selection.

Automated Text Classification of Maintenance Data of Higher Education Buildings Using Text Mining and Machine Learning Techniques

Data-driven analysis and machine learning for energy prediction in distributed photovoltaic generation plants: a case study in queensland, australia, modeling nutrient removal by membrane bioreactor at a sewage treatment plant using machine learning models, big five personality prediction based in indonesian tweets using machine learning methods.

<span lang="EN-US">The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including <a name="_Hlk87278444"></a>naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.</span>

Compressive strength of concrete with recycled aggregate; a machine learning-based evaluation

Temperature prediction of flat steel box girders of long-span bridges utilizing in situ environmental parameters and machine learning, computer-assisted cohort identification in practice.

The standard approach to expert-in-the-loop machine learning is active learning, where, repeatedly, an expert is asked to annotate one or more records and the machine finds a classifier that respects all annotations made until that point. We propose an alternative approach, IQRef , in which the expert iteratively designs a classifier and the machine helps him or her to determine how well it is performing and, importantly, when to stop, by reporting statistics on a fixed, hold-out sample of annotated records. We justify our approach based on prior work giving a theoretical model of how to re-use hold-out data. We compare the two approaches in the context of identifying a cohort of EHRs and examine their strengths and weaknesses through a case study arising from an optometric research problem. We conclude that both approaches are complementary, and we recommend that they both be employed in conjunction to address the problem of cohort identification in health research.