ArticleGenome research2019
Anchor: trans-cell type prediction of transcription factor binding sites.
Article in Genome research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- EpiXFormer: a cross-attention neural network for predicting cell type-specific transcription factor binding sites.Briefings in bioinformatics · 2026Article
- Modeling strategies forBioinformatics advances · 2026Article
- Unmeasured human transcription factor ChIP-seq data shape functional genomics and demand strategic prioritization.Briefings in functional genomics · 2025Article
- Predicting CTCF cell type active binding sites in human genome.Scientific reports · 2024Article
- REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- Interpretable predictive models of genome-wide aryl hydrocarbon receptor-DNA binding reveal tissue-specific binding determinants.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
- A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.Nucleic acids research · 2023Article
- Correction of transposase sequence bias in ATAC-seq data with rule ensemble modeling.NAR genomics and bioinformatics · 2023Article
- Cell type-specific interpretation of noncoding variants using deep learning-based methods.GigaScience · 2023Article
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
- Benefiting from the intrinsic role of epigenetics to predict patterns of CTCF binding.Computational and structural biotechnology journal · 2023Article
- Computational prediction and characterization of cell-type-specific and shared binding sites.Bioinformatics (Oxford, England) · 2023Article
- maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.PLoS computational biology · 2023Article
- Evaluating deep learning for predicting epigenomic profiles.Nature machine intelligence · 2022Article
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
- NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction.Bioinformatics (Oxford, England) · 2022Article
- Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.Genome research · 2022Article
- Asymmetric Predictive Relationships Across Histone Modifications.Nature machine intelligence · 2022Article
- Decoding disease: from genomes to networks to phenotypes.Nature reviews. Genetics · 2021Review
- Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning.Nucleic acids research · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The ENCyclopedia of DNA Elements (ENCODE) consortium has generated transcription factor (TF) binding ChIP-seq data covering hundreds of TF proteins and cell types; however, due to limits on time and resources, only a small fraction of all possible TF-cell type pairs have been profiled. One solution is to build machine learning models trained on currently available epigenomic data sets that can be applied to the remaining missing pairs. A major challenge is that TF binding sites are cell-type-specific, which can be attributed to cellular contexts such as chromatin accessibility. Meanwhile, indirect TF-DNA binding and interactions between TFs complicate this regulatory process. Technical issues such as sequencing biases and batch effects render the prediction task even more challenging. Many pioneering efforts have been made to predict TF binding profiles based on DNA sequence and DNase-seq footprints, but to what extent a model can be generalized to completely untested cell conditions remains unknown. In this study, we describe our first place solution to the 2017 ENCODE-DREAM in vivo TF binding site prediction challenge. By carefully addressing multisource biases and information imbalance across cell types, we created a pipeline that significantly outperforms the current state-of-the-art methods. The proposed method is sufficiently complex enough to model nonlinear interactions between TF binding motifs and chromatin accessibility information up to 1500 bp from the genomic region of interest.
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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.