ArticleBioinformatics (Oxford, England)2018
Base-pair resolution detection of transcription factor binding site by deep deconvolutional network.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- EvoSNR-Prom: Predicting promoters at single-nucleotide resolution with label-aware transfer learning of the pretrained EVO model.PLoS computational biology · 2026Article
- NyxBind: enhancing deep neural representations for transcription factor binding site prediction via contrastive learning.Briefings in bioinformatics · 2026Article
- A Deep Learning Framework with Multi-perspective Feature Fusion for Transcription Factor Binding Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Predicting TF-Target Gene Association Using a Heterogeneous Network and Enhanced Negative Sampling.Bioinformatics and biology insights · 2025Article
- The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes.BMC bioinformatics · 2024Article
- BertSNR: an interpretable deep learning framework for single-nucleotide resolution identification of transcription factor binding sites based on DNA language model.Bioinformatics (Oxford, England) · 2024Article
- Predicting Transcription Factor Binding Sites with Deep Learning.International journal of molecular sciences · 2024Article
- Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.PLoS computational biology · 2023Article
- NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction.Bioinformatics (Oxford, England) · 2022Article
- Prediction and Experimental Validation of a New Salinity-Responsive Cis-Regulatory Element (CRE) in a Tilapia Cell Line.Life (Basel, Switzerland) · 2022Article
- Dissecting and predicting different types of binding sites in nucleic acids based on structural information.Briefings in bioinformatics · 2022Article
- Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.Briefings in bioinformatics · 2022Article
- Phage_UniR_LGBM: Phage Virion Proteins Classification with UniRep Features and LightGBM Model.Computational and mathematical methods in medicine · 2022Article
- Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks.Frontiers in physics · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Motivation: Transcription factor (TF) binds to the promoter region of a gene to control gene expression. Identifying precise TF binding sites (TFBSs) is essential for understanding the detailed mechanisms of TF-mediated gene regulation. However, there is a shortage of computational approach that can deliver single base pair resolution prediction of TFBS. Results: In this paper, we propose DeepSNR, a Deep Learning algorithm for predicting TF binding location at Single Nucleotide Resolution de novo from DNA sequence. DeepSNR adopts a novel deconvolutional network (deconvNet) model and is inspired by the similarity to image segmentation by deconvNet. The proposed deconvNet architecture is constructed on top of 'DeepBind' and we trained the entire model using TF-specific data from ChIP-exonuclease (ChIP-exo) experiments. DeepSNR has been shown to outperform motif search-based methods for several evaluation metrics. We have also demonstrated the usefulness of DeepSNR in the regulatory analysis of TFBS as well as in improving the TFBS prediction specificity using ChIP-seq data. Availability and implementation: DeepSNR is available open source in the GitHub repository (https://github.com/sirajulsalekin/DeepSNR). Supplementary information: Supplementary data are available at Bioinformatics online.
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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.