ReviewNature reviews. Genetics2015
Machine learning applications in genetics and genomics.
Review in Nature reviews. Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 767 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
767 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for multi-omics data integration in crop improvement: a systematic review.BMC bioinformatics · 2026Pooled it
- Metabolic reprogramming in plant defense: linking signaling networks to metabolomics-driven insights.Plant signaling & behavior · 2026Review
- Article
- Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.Nature methods · 2026Article
- Trends in Plant Breeding and Genetics.International journal of molecular sciences · 2026Article
- Nutrigenomics and Epigenetic Regulation in Cancer Prevention: Molecular Mechanisms, Dietary Bioactives, and Translational Perspectives.Nutrients · 2026Review
- Special Issue "Machine Learning and Bioinformatics in Human Health and Disease 2nd Edition".International journal of molecular sciences · 2026Article
- Ferroptosis Signatures in Diabetic Cardiomyopathy: Multi-Omics Discovery and Validation of ACOT1 and TXNIP.Current issues in molecular biology · 2026Article
- Enhancing detection of polygenic adaptation: a comparative study of machine learning and statistical approaches using simulated evolve-and-resequence data.BMC genomics · 2026Article
- Diagnosis and classification of thalassemia disease using machine learning: Comparative analysis of traditional models and a novel hybrid approach.Technology and health care : official journal of the European Society for Engineering and Medicine · 2026Article
- A high-dimensional benchmark of objective functions and biological resolutions for personalized metabolic phenotyping.Briefings in bioinformatics · 2026Article
- Machine learning-based prediction of cross-immunity.Briefings in bioinformatics · 2026Article
- NADPH oxidases in immunometabolism and disease pathology: mechanistic networks, pollutant triggers, and therapeutic frontiers.Cellular & molecular immunology · 2026Review
- A machine-learning model for accurate diuresis prediction in acute heart failure (DRC-AHF).ESC heart failure · 2026Article
- Article
- Computational Genomics for Resistome Characterization: Current Advancements and Future Challenges Under a One Health Perspective.Antibiotics (Basel, Switzerland) · 2026Review
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Discovery of a phenazine-thiol conjugase from sparse data using genome-informed machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Machine Learning-Guided Stress Atlases Reveal Co-Expression Rewiring and Divergent Cellular Deployment of Abiotic Stress Programs in Rice and Wheat.International journal of molecular sciences · 2026Article
- Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation.Bioinformatics (Oxford, England) · 2026Article
707 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.