ArticleMolecular therapy. Nucleic acids2021
Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning architecture.
Article in Molecular therapy. Nucleic acids, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Nonconsensus flanking sequence of hundreds of base pairs around in vivo binding sites: statistical beacons for transcription factor scanning.Nucleic acids research · 2026Article
- A DNABERT based deep learning framework for predicting transcription factor binding sites.Scientific reports · 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
- Inferring binding specificities of human transcription factors with the wisdom of crowds.bioRxiv : the preprint server for biology · 2025Article
- TRAFICA: an open chromatin language model to improve transcription factor binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- AI-Driven Biomarker Discovery and Personalized Allergy Treatment: Utilizing Machine Learning and NGS.Current allergy and asthma reports · 2025Review
- Enhancers in Plant Development, Adaptation and Evolution.Plant & cell physiology · 2025Review
- DeepMEns: an ensemble model for predicting sgRNA on-target activity based on multiple features.Briefings in functional genomics · 2025Article
- RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.Briefings in bioinformatics · 2024Article
- MLSNet: a deep learning model for predicting transcription factor binding sites.Briefings in bioinformatics · 2024Article
- Deep DNAshape webserver: prediction and real-time visualization of DNA shape considering extended k-mers.Nucleic acids research · 2024Article
- Predicting Transcription Factor Binding Sites with Deep Learning.International journal of molecular sciences · 2024Article
- BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.Briefings in bioinformatics · 2024Article
- Predicting DNA structure using a deep learning method.Nature communications · 2024Article
- Harnessing regulatory networks in Actinobacteria for natural product discovery.Journal of industrial microbiology & biotechnology · 2024Review
- Prediction of mammalian tissue-specific CLOCK-BMAL1 binding to E-box DNA motifs.Scientific reports · 2023Article
- HAMPLE: deciphering TF-DNA binding mechanism in different cellular environments by characterizing higher-order nucleotide dependency.Bioinformatics (Oxford, England) · 2023Article
- Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases.International journal of molecular sciences · 2021Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of transcriptional regulation is still difficult yet fundamental in molecular biology research. Recent research has shown that the double helix structure of nucleotides plays an important role in improving the accuracy and interpretability of transcription factor binding sites (TFBSs). Although several computational methods have been designed to take both DNA sequence and DNA shape features into consideration simultaneously, how to design an efficient model is still an intractable topic. In this paper, we proposed a hybrid convolutional recurrent neural network (CNN/RNN) architecture, CRPTS, to predict TFBSs by combining DNA sequence and DNA shape features. The novelty of our proposed method relies on three critical aspects: (1) the application of a shared hybrid CNN and RNN has the ability to efficiently extract features from large-scale genomic sequences obtained by high-throughput technology; (2) the common patterns were found from DNA sequences and their corresponding DNA shape features; (3) our proposed CRPTS can capture local structural information of DNA sequences without completely relying on DNA shape data. A series of comprehensive experiments on 66
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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 Socratic graph.