ArticlePloS one2019
An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences.
Article in PloS one, 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.
- caRBP-Pred: Leveraging Protein Language Models for the Prediction of Chromatin-Associated RNA-Binding Proteins.Computational and structural biotechnology journal · 2026Article
- A descriptor-free machine learning framework to improve antigen discovery for bacterial pathogens.PloS one · 2025Article
- Accurate prediction of nucleic acid binding proteins using protein language model.Bioinformatics advances · 2025Article
- DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity.BMC bioinformatics · 2024Article
- Improved prediction of DNA and RNA binding proteins with deep learning models.Briefings in bioinformatics · 2024Article
- StackDPP: a stacking ensemble based DNA-binding protein prediction model.BMC bioinformatics · 2024Article
- Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria.F1000Research · 2024Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- Generative models for protein sequence modeling: recent advances and future directions.Briefings in bioinformatics · 2023Review
- Functional annotation of proteins for signaling network inference in non-model species.Nature communications · 2023Article
- Sequence and thermodynamic characteristics of terminators revealed by FlowSeq and the discrimination of terminators strength.Synthetic and systems biotechnology · 2022Article
- Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches.Biomolecules · 2022Review
- Deep Neural Network Framework Based on Word Embedding for Protein Glutarylation Sites Prediction.Life (Basel, Switzerland) · 2022Article
- AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning.BMC bioinformatics · 2022Article
- Research on the Improved CNN Deep Learning Method for Motion Intention Recognition of Dynamic Lower Limb Prosthesis.Journal of healthcare engineering · 2021Article
- Deep Learning for SARS COV-2 Genome Sequences.IEEE access : practical innovations, open solutions · 2021Article
- Article
- PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method.BioMed research international · 2020Article
- HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2020Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
As the number of known proteins has expanded, how to accurately identify DNA binding proteins has become a significant biological challenge. At present, various computational methods have been proposed to recognize DNA-binding proteins from only amino acid sequences, such as SVM, DNABP and CNN-RNN. However, these methods do not consider the context in amino acid sequences, which makes it difficult for them to adequately capture sequence features. In this study, a new method that coordinates a bidirectional long-term memory recurrent neural network and a convolutional neural network, called CNN-BiLSTM, is proposed to identify DNA binding proteins. The CNN-BiLSTM model can explore the potential contextual relationships of amino acid sequences and obtain more features than can traditional models. The experimental results show that the CNN-BiLSTM achieves a validation set prediction accuracy of 96.5%-7.8% higher than that of SVM, 9.6% higher than that of DNABP and 3.7% higher than that of CNN-RNN. After testing on 20,000 independent samples provided by UniProt that were not involved in model training, the accuracy of CNN-BiLSTM reached 94.5%-12% higher than that of SVM, 4.9% higher than that of DNABP and 4% higher than that of CNN-RNN. We visualized and compared the model training process of CNN-BiLSTM with that of CNN-RNN and found that the former is capable of better generalization from the training dataset, showing that CNN-BiLSTM has a wider range of adaptations to protein sequences. On the test set, CNN-BiLSTM has better credibility because its predicted scores are closer to the sample labels than are those of CNN-RNN. Therefore, the proposed CNN-BiLSTM is a more powerful method for identifying DNA-binding proteins.
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