ArticleGenome biology2020
A pitfall for machine learning methods aiming to predict across cell types.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 55 citations in OpenAlex.
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- Trimodal single-cell profiling of transcriptome, epigenome and 3D genome in complex tissues with scHiCAR.Nature biotechnology · 2026Article
- Developing a general AI model for integrating diverse genomic modalities and comprehensive genomic knowledge.Nucleic acids research · 2025Article
- Iterative improvement of deep learning models using synthetic regulatory genomics.Genome research · 2025Article
- Cutting-edge technologies in neural regeneration.Cell regeneration (London, England) · 2025Review
- Developing a general AI model for integrating diverse genomic modalities and comprehensive genomic knowledge.bioRxiv : the preprint server for biology · 2025Article
- Loss of MEF2C function by enhancer mutation leads to neuronal mitochondria dysfunction and motor deficits in mice.Molecular neurodegeneration · 2025Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Best holdout assessment is sufficient for cancer transcriptomic model selection.Patterns (New York, N.Y.) · 2024Article
- Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.Nature communications · 2024Article
- dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility.Briefings in bioinformatics · 2024Article
- Predicting gene expression state and prioritizing putative enhancers using 5hmC signal.Genome biology · 2024Article
- Article
- Article
- Integrative modeling of lncRNA-chromatin interaction maps reveals diverse mechanisms of nuclear retention.BMC genomics · 2023Article
- A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.Nucleic acids research · 2023Article
- UNADON: transformer-based model to predict genome-wide chromosome spatial position.Bioinformatics (Oxford, England) · 2023Article
- The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles.Genome biology · 2023Article
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Machine learning models that predict genomic activity are most useful when they make accurate predictions across cell types. Here, we show that when the training and test sets contain the same genomic loci, the resulting model may falsely appear to perform well by effectively memorizing the average activity associated with each locus across the training cell types. We demonstrate this phenomenon in the context of predicting gene expression and chromatin domain boundaries, and we suggest methods to diagnose and avoid the pitfall. We anticipate that, as more data becomes available, future projects will increasingly risk suffering from this issue.
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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 Socratic graph.