ArticleCurrent opinion in systems biology2020
Deep learning for inferring transcription factor binding sites.
Article in Current opinion in systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.Briefings in bioinformatics · 2023Pooled it
- From conservation scores to language models: a feature engineering perspective on machine learning for genetic variant interpretation.Briefings in bioinformatics · 2026Review
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Using Deep Learning Models of Gene Regulation to Guide Drug Prioritization.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Transcription clusters and developmental pathways - nature, nurture, noise.Journal of cell science · 2026Review
- Informational blueprints reveal condition-dependent gene regulatory architectures.bioRxiv : the preprint server for biology · 2026Article
- Using Deep Learning Models of Gene Regulation to Guide Drug Prioritization.bioRxiv : the preprint server for biology · 2026Article
- Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion.bioRxiv : the preprint server for biology · 2026Article
- Uncovering the Mechanistic Landscape of Regulatory DNA with Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- ProPr54 web server: predicting σNAR genomics and bioinformatics · 2025Article
- A generative framework for enhanced cell-type specificity in rationally designed mRNAs.bioRxiv : the preprint server for biology · 2024Article
- Predicting CTCF cell type active binding sites in human genome.Scientific reports · 2024Article
- InterpretingNature machine intelligence · 2024Article
- Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.Bioinformatics (Oxford, England) · 2024Article
- EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.Bioinformatics (Oxford, England) · 2024Article
- InterpretingbioRxiv : the preprint server for biology · 2024Article
- EvoAug-TF: Extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.bioRxiv : the preprint server for biology · 2024Article
- Multiomics-integrated deep language model enables in silico genome-wide detection of transcription factor binding site in unexplored biosamples.Bioinformatics (Oxford, England) · 2024Article
- Advancing Regulatory Genomics With Machine Learning.Bioinformatics and biology insights · 2024Review
- Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks.BMC genomics · 2023Article
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
Deep learning is a powerful tool for predicting transcription factor binding sites from DNA sequence. Despite their high predictive accuracy, there are no guarantees that a high-performing deep learning model will learn causal sequence-function relationships. Thus a move beyond performance comparisons on benchmark datasets is needed. Interpreting model predictions is a powerful approach to identify which features drive performance gains and ideally provide insight into the underlying biological mechanisms. Here we highlight timely advances in deep learning for genomics, with a focus on inferring transcription factors binding sites. We describe recent applications, model architectures, and advances in
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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 OpenQuestion graph.