ArticleInternational journal of molecular sciences2021
DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- AI-driven discovery of dual antiaging and anti-AD therapeutics via PROTAC target deconvolution of a super-enhancer-regulated axis.Science advances · 2025Article
- A KAN-based hybrid deep neural networks for accurate identification of transcription factor binding sites.PloS one · 2025Article
- Predicting CTCF cell type active binding sites in human genome.Scientific reports · 2024Article
- BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.RNA biology · 2024Article
- Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction.Biosafety and health · 2023Article
- iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework.BMC bioinformatics · 2022Article
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Authors and funding
4 authors.
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Abstract
Predicting in vivo protein-DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been identified by biochemical experiments that are time-consuming and laborious. To tackle this challenge, many computational methods have been proposed to predict TF binding sites from DNA sequence. Although previous methods have achieved remarkable performance in the prediction of protein-DNA interactions, there is still considerable room for improvement. In this paper, we present a hybrid deep learning framework, termed DeepD2V, for transcription factor binding sites prediction. First, we construct the input matrix with an original DNA sequence and its three kinds of variant sequences, including its inverse, complementary, and complementary inverse sequence. A sliding window of size
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