ArticlePLoS computational biology2017
Imputation for transcription factor binding predictions based on deep learning.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed, 112 citations in OpenAlex.
- How negative sampling shapes the performance of transcription factor binding site prediction models.Bioinformatics (Oxford, England) · 2026Article
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- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Integrating Prior Knowledge Using Transformer for Gene Regulatory Network Inference.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes.BMC bioinformatics · 2024Article
- Predicting Transcription Factor Binding Sites with Deep Learning.International journal of molecular sciences · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Screening for functional regulatory variants in open chromatin using GenIE-ATAC.Nucleic acids research · 2023Article
- The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles.Genome biology · 2023Article
- Evidence for the role of transcription factors in the co-transcriptional regulation of intron retention.Genome biology · 2023Article
- Deep learning-empowered crop breeding: intelligent, efficient and promising.Frontiers in plant science · 2023Article
- An Evidence Theory and Fuzzy Logic Combined Approach for the Prediction of Potential ARF-Regulated Genes in Quinoa.Plants (Basel, Switzerland) · 2022Article
- NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction.Bioinformatics (Oxford, England) · 2022Article
- Base-resolution prediction of transcription factor binding signals by a deep learning framework.PLoS computational biology · 2022Article
- Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.Briefings in bioinformatics · 2022Article
- Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation.PloS one · 2022Article
- Decoding disease: from genomes to networks to phenotypes.Nature reviews. Genetics · 2021Review
- A self-attention model for inferring cooperativity between regulatory features.Nucleic acids research · 2021Article
- DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.International journal of molecular sciences · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding.
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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.