ArticleScientific reports2019
Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network.
Article in Scientific reports, 2019. 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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19 citing papers in PubMed.
- A scalable computational framework for predicting gene expression from candidateGenome research · 2026Article
- DNA-Protein Binding is Dominated by Short Anchoring Elements.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Modeling and designing enhancers by introducing and harnessing transcription factor binding units.Nature communications · 2025Article
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.Briefings in bioinformatics · 2024Article
- Coding genomes with gapped pattern graph convolutional network.Bioinformatics (Oxford, England) · 2024Article
- Classification of DNA Sequence Based on a Non-gradient Algorithm: Pseudoinverse Learners.Methods in molecular biology (Clifton, N.J.) · 2024Article
- KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.Journal of translational medicine · 2023Article
- 4acCPred: Weakly supervised prediction ofMolecular therapy. Nucleic acids · 2022Article
- Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.Briefings in bioinformatics · 2022Article
- Genomics enters the deep learning era.PeerJ · 2022Review
- Prediction of Transcription Factor Binding Sites Using a Combined Deep Learning Approach.Frontiers in oncology · 2022Article
- Locating transcription factor binding sites by fully convolutional neural network.Briefings in bioinformatics · 2021Article
- HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid-protein interface with a case study.Briefings in bioinformatics · 2021Article
- A survey on deep learning in DNA/RNA motif mining.Briefings in bioinformatics · 2021Article
- Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data.Bioinformatics (Oxford, England) · 2021Article
- DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.International journal of molecular sciences · 2021Article
- Prediction Model of Organic Molecular Absorption Energies based on Deep Learning trained by Chaos-enhanced Accelerated Evolutionary algorithm.Scientific reports · 2019Article
- A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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3 authors.
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Abstract
Modeling in-vivo protein-DNA binding is not only fundamental for further understanding of the regulatory mechanisms, but also a challenging task in computational biology. Deep-learning based methods have succeed in modeling in-vivo protein-DNA binding, but they often (1) follow the fully supervised learning framework and overlook the weakly supervised information of genomic sequences that a bound DNA sequence may has multiple TFBS(s), and, (2) use one-hot encoding to encode DNA sequences and ignore the dependencies among nucleotides. In this paper, we propose a weakly supervised framework, which combines multiple-instance learning with a hybrid deep neural network and uses k-mer encoding to transform DNA sequences, for modeling in-vivo protein-DNA binding. Firstly, this framework segments sequences into multiple overlapping instances using a sliding window, and then encodes all instances into image-like inputs of high-order dependencies using k-mer encoding. Secondly, it separately computes a score for all instances in the same bag using a hybrid deep neural network that integrates convolutional and recurrent neural networks. Finally, it integrates the predicted values of all instances as the final prediction of this bag using the Noisy-and method. The experimental results on in-vivo datasets demonstrate the superior performance of the proposed framework. In addition, we also explore the performance of the proposed framework when using k-mer encoding, and demonstrate the performance of the Noisy-and method by comparing it with other fusion methods, and find that adding recurrent layers can improve the performance of the proposed framework.
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