ArticleNature machine intelligence2021
Deep neural networks identify sequence context features predictive of transcription factor binding.
Article in Nature machine intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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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.
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.
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Who cites it
49 citing papers in PubMed.
- seqLens: Optimizing Language Models for Genomic Predictions.Molecular biology and evolution · 2026Article
- PATTY corrects open-chromatin bias for improved bulk and single-cell CUT&Tag profiling.Nature communications · 2026Article
- Sequence-based modeling of low-affinity transcription factor-DNA binding through deep learning.NAR genomics and bioinformatics · 2026Article
- A Deep Learning Framework with Multi-perspective Feature Fusion for Transcription Factor Binding Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- Synthetic tunable promoters for flexible control of multi-gene expression in mammalian cells.Journal of advanced research · 2025Article
- Multiscale metabolic engineering in biological lignin valorization.Innovation (Cambridge (Mass.)) · 2025Review
- PATTY corrects open-chromatin bias for improved bulk and single-cell CUT&Tag profiling.Research square · 2025Article
- PATTY corrects open chromatin bias for improved bulk and single-cell CUT&Tag profiling.bioRxiv : the preprint server for biology · 2025Article
- Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts.ArXiv · 2025Article
- CREATE: cell-type-specific cis-regulatory element identification via discrete embedding.Nature communications · 2025Article
- Modeling and designing enhancers by introducing and harnessing transcription factor binding units.Nature communications · 2025Article
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- Identifying transcription factors with cell-type specific DNA binding signatures.BMC genomics · 2024Article
- MLSNet: a deep learning model for predicting transcription factor binding sites.Briefings in bioinformatics · 2024Article
- PRONTO-TK: a user-friendly PROtein Neural neTwOrk tool-kit for accessible protein function prediction.NAR genomics and bioinformatics · 2024Article
- Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning.Scientific data · 2024Article
- TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.Genome biology · 2024Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Transcription factors (TFs) bind DNA by recognizing specific sequence motifs, typically of length 6-12bp. A motif can occur many thousands of times in the human genome, but only a subset of those sites are actually bound. Here we present a machine learning framework leveraging existing convolutional neural network architectures and model interpretation techniques to identify and interpret sequence context features most important for predicting whether a particular motif instance will be bound. We apply our framework to predict binding at motifs for 38 TFs in a lymphoblastoid cell line, score the importance of context sequences at base-pair resolution, and characterize context features most predictive of binding. We find that the choice of training data heavily influences classification accuracy and the relative importance of features such as open chromatin. Overall, our framework enables novel insights into features predictive of TF binding and is likely to inform future deep learning applications to interpret non-coding genetic variants.
Identifiers
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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.