ArticlePeerJ2021
Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning.
Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The trial behind it
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Who cites it
16 citing papers in PubMed, 47 citations in OpenAlex.
- caRBP-Pred: Leveraging Protein Language Models for the Prediction of Chromatin-Associated RNA-Binding Proteins.Computational and structural biotechnology journal · 2026Article
- Interpretable prediction of nucleic acid-binding proteins using a protein language model.Bioinformatics advances · 2026Article
- ProGraphTrans: multimodal dynamic collaborative framework for protein representation learning.BMC biology · 2025Article
- DRBP-EDP: classification of DNA-binding proteins and RNA-binding proteins using ESM-2 and dual-path neural network.NAR genomics and bioinformatics · 2025Article
- PLM-DBPs: enhancing plant DNA-binding protein prediction by integrating sequence-based and structure-aware protein language models.Briefings in bioinformatics · 2025Article
- DeepRice6mA: A convolutional neural network approach for 6mA site prediction in the rice Genome.PloS one · 2025Article
- Benchmarking recent computational tools for DNA-binding protein identification.Briefings in bioinformatics · 2024Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.Briefings in bioinformatics · 2024Article
- Article
- Transcription factor-based biosensors for screening and dynamic regulation.Frontiers in bioengineering and biotechnology · 2023Review
- Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches.Biomolecules · 2022Review
- Research on DNA-Binding Protein Identification Method Based on LSTM-CNN Feature Fusion.Computational and mathematical methods in medicine · 2022Article
- Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks.Frontiers in pharmacology · 2022Article
- DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.
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Registered trials
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 OpenQuestion graph.