ArticleBMC bioinformatics2019
iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks.
Article in BMC bioinformatics, 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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Who cites it
19 citing papers in PubMed.
- BeitAI-pHLA: Multiallele Peptide-HLA Class I Binding Prediction Using Protein Language Model and Multi-Instance Learning.Computational and structural biotechnology journal · 2026Article
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- TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.Communications biology · 2025Article
- iProtDNA-SMOTE: Enhancing protein-DNA binding sites prediction through imbalanced graph neural networks.PloS one · 2025Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein.Nature communications · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites.BMC genomics · 2024Article
- ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site prediction.Briefings in bioinformatics · 2024Article
- Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search.PeerJ. Computer science · 2024Article
- CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks.Communications biology · 2023Article
- i4mC-GRU: Identifying DNA NComputational and structural biotechnology journal · 2023Article
- eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towardsComputational and structural biotechnology journal · 2023Article
- iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features.BMC genomics · 2022Article
- Identifying Transcription Factors That Prefer Binding to Methylated DNA Using ReducedACS omega · 2022Article
- Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction.BMC bioinformatics · 2022Article
- MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network.Biomolecules · 2021Article
- DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.International journal of molecular sciences · 2021Article
- Identifying the sequence specificities of circRNA-binding proteins based on a capsule network architecture.BMC bioinformatics · 2021Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in the age of post-genomic where data on protein sequences have expanded very fast. In this study, we propose iProDNA-CapsNet - a new prediction model identifying protein-DNA binding residues using an ensemble of capsule neural networks (CapsNets) on position specific scoring matrix (PSMM) profiles. The use of CapsNets promises an innovative approach to determine the location of DNA-binding residues. In this study, the benchmark datasets introduced by Hu et al. (2017), i.e., PDNA-543 and PDNA-TEST, were used to train and evaluate the model, respectively. To fairly assess the model performance, comparative analysis between iProDNA-CapsNet and existing state-of-the-art methods was done.
resultsUnder the decision threshold corresponding to false positive rate (FPR) ≈ 5%, the accuracy, sensitivity, precision, and Matthews's correlation coefficient (MCC) of our model is increased by about 2.0%, 2.0%, 14.0%, and 5.0% with respect to TargetDNA (Hu et al., 2017) and 1.0%, 75.0%, 45.0%, and 77.0% with respect to BindN+ (Wang et al., 2010), respectively. With regards to other methods not reporting their threshold settings, iProDNA-CapsNet also shows a significant improvement in performance based on most of the evaluation metrics. Even with different patterns of change among the models, iProDNA-CapsNets remains to be the best model having top performance in most of the metrics, especially MCC which is boosted from about 8.0% to 220.0%.
conclusionsAccording to all evaluation metrics under various decision thresholds, iProDNA-CapsNet shows better performance compared to the two current best models (BindN and TargetDNA). Our proposed approach also shows that CapsNet can potentially be used and adopted in other biological applications.
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