ArticleMethods (San Diego, Calif.)2019
FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.
Article in Methods (San Diego, Calif.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers.
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
99 citing papers in PubMed.
- Genome-wide modelling of plant transcription factor binding captures regulatory variants associated with phenotypic traits.Nature communications · 2026Article
- TPCAV: Interpreting deep learning genomics models via concept attribution.bioRxiv : the preprint server for biology · 2026Article
- An end-to-end generalizable deep learning framework to comprehensively analyze transcriptional regulation.Nature communications · 2026Article
- How negative sampling shapes the performance of transcription factor binding site prediction models.Bioinformatics (Oxford, England) · 2026Article
- EpiXFormer: a cross-attention neural network for predicting cell type-specific transcription factor binding sites.Briefings in bioinformatics · 2026Article
- Computational cytology: Bridging microscopy and artificial intelligence.CytoJournal · 2026Article
- Modeling strategies forBioinformatics advances · 2026Article
- Deep learning application for genomic data analysis.BMB reports · 2026Review
- mwHIT: accelerated and accurate histone modification imputation using multi-scale window attention.Frontiers in genetics · 2026Article
- Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli.Nature communications · 2025Article
- Kolmogorov-Arnold networks for genomic tasks.Briefings in bioinformatics · 2025Article
- A multi-modal transformer for cell type-agnostic regulatory predictions.Cell genomics · 2025Article
- DNA breathing integration with deep learning foundational model advances genome-wide binding prediction of human transcription factors.Nucleic acids research · 2024Article
- Identifying transcription factors with cell-type specific DNA binding signatures.BMC genomics · 2024Article
- Review
- Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling.Proceedings of machine learning research · 2024Article
- REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- Deep learning the cis-regulatory code for gene expression in selected model plants.Nature communications · 2024Article
- MMGAT: a graph attention network framework for ATAC-seq motifs finding.BMC bioinformatics · 2024Article
- GNNMF: a multi-view graph neural network for ATAC-seq motif finding.BMC genomics · 2024Article
39 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
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, called FactorNet, to computationally impute the missing binding data. FactorNet trains on binding data from reference cell types to make predictions on testing cell types by leveraging a variety of features, including genomic sequences, genome annotations, gene expression, and signal data, such as DNase I cleavage. FactorNet implements several convenient strategies to reduce runtime and memory consumption. By visualizing the neural network models, we can interpret how the model predicts binding. We also investigate the variables that affect cross-cell type accuracy, and offer suggestions to improve upon this field. Our method ranked among the top teams in the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge, achieving first place on six of the 13 final round evaluation TF/cell type pairs, the most of any competing team. The FactorNet source code is publicly available, allowing users to reproduce our methodology from the ENCODE-DREAM Challenge.
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.