ReviewNature reviews. Genetics2022
Navigating the pitfalls of applying machine learning in genomics.
Review in Nature reviews. Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 papers, 2 of them syntheses 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
150 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges.Frontiers in bioinformatics · 2026Pooled it
- Machine learning-based meta-analysis reveals gut microbiome alterations associated with Parkinson's disease.Nature communications · 2025Pooled it
- Codon optimality predicts mRNA half-life but does not transfer to lncRNAs.Molecular genetics and genomics : MGG · 2026Article
- Cell-level random splits leak group-owned answers in single-cell benchmarks.bioRxiv : the preprint server for biology · 2026Article
- Review
- Cross-Model Uncertainty-Aware Minimal Editing of Cis-Regulatory Elements for Cell-Type-Selective Design.Genes · 2026Article
- Response to comment on "Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper".PLoS computational biology · 2026Article
- Developing SCL2205: a protein sequence-based spatial modelling dataset for the protein language model frontier.Bioinformatics (Oxford, England) · 2026Article
- Generalizable cancer detection from ultra-low-pass WGS via deep contextual modeling of cfDNA sequences.Molecular biomedicine · 2026Article
- Harnessing Genomic Information for Identifying the Geographic Origin of Five North American Tree Species in Trade.Evolutionary applications · 2026Article
- Leveraging artificial intelligence to enhance marine biosecurity.Bioscience · 2026Review
- A Boruta-SMOTE Integrated Approach for Rapid Donkey Breed Classification Using SNP Data: Addressing High-Dimensionality and Small Sample Challenges.Biochemical genetics · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Integrating Mutation-Derived and Expression Features from Single-Cell RNA Sequencing: Pitfalls of Standard Cross-Validation in Small-Cohort Settings.International journal of molecular sciences · 2026Article
- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- TF-GateNet: An Interpretable and Biologically Guided Framework for Primary-Metastatic State Prediction from Somatic Genomic Alterations.Biomolecules · 2026Article
- The evolution of AI-integrated genome editing and its challenges.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Review
- Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.Briefings in bioinformatics · 2026Review
- FAIR4prep: FAIR clinical informatics data preprocessing in artificial intelligence applications.Scientific data · 2026Article
90 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the application of supervised learning in genomics research. However, the assumptions behind the statistical models and performance evaluations in ML software frequently are not met in biological systems. In this Review, we illustrate the impact of several common pitfalls encountered when applying supervised ML in genomics. We explore how the structure of genomics data can bias performance evaluations and predictions. To address the challenges associated with applying cutting-edge ML methods to genomics, we describe solutions and appropriate use cases where ML modelling shows great potential.
Indexed as
Identifiers
34837041What 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.