ArticlePloS one2017
On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 71 citations in OpenAlex.
- DRBP-EDP: classification of DNA-binding proteins and RNA-binding proteins using ESM-2 and dual-path neural network.NAR genomics and bioinformatics · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Accurate prediction of nucleic acid binding proteins using protein language model.Bioinformatics advances · 2025Article
- Benchmarking recent computational tools for DNA-binding protein identification.Briefings in bioinformatics · 2024Article
- Improved prediction of DNA and RNA binding proteins with deep learning models.Briefings in bioinformatics · 2024Article
- Melatonin and multiple sclerosis: antioxidant, anti-inflammatory and immunomodulator mechanism of action.Inflammopharmacology · 2022Review
- Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches.Biomolecules · 2022Review
- Predicting residues involved in anti-DNA autoantibodies with limited neural networks.Medical & biological engineering & computing · 2022Article
- A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification.PloS one · 2022Article
- LABAMPsGCN: A framework for identifying lactic acid bacteria antimicrobial peptides based on graph convolutional neural network.Frontiers in genetics · 2022Article
- Research on DNA-Binding Protein Identification Method Based on LSTM-CNN Feature Fusion.Computational and mathematical methods in medicine · 2022Article
- BERT-PPII: The Polyproline Type II Helix Structure Prediction Model Based on BERT and Multichannel CNN.BioMed research international · 2022Article
- Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments.Analytical chemistry · 2021Article
- Article
- VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost.Frontiers in genetics · 2021Article
- 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
- An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences.PloS one · 2019Article
- RNA-protein interactions in an unstructured context.FEBS letters · 2018Review
- Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA-binding proteins play pivotal roles in alternative splicing, RNA editing, methylating and many other biological functions for both eukaryotic and prokaryotic proteomes. Predicting the functions of these proteins from primary amino acids sequences is becoming one of the major challenges in functional annotations of genomes. Traditional prediction methods often devote themselves to extracting physiochemical features from sequences but ignoring motif information and location information between motifs. Meanwhile, the small scale of data volumes and large noises in training data result in lower accuracy and reliability of predictions. In this paper, we propose a deep learning based method to identify DNA-binding proteins from primary sequences alone. It utilizes two stages of convolutional neutral network to detect the function domains of protein sequences, and the long short-term memory neural network to identify their long term dependencies, an binary cross entropy to evaluate the quality of the neural networks. When the proposed method is tested with a realistic DNA binding protein dataset, it achieves a prediction accuracy of 94.2% at the Matthew's correlation coefficient of 0.961. Compared with the LibSVM on the arabidopsis and yeast datasets via independent tests, the accuracy raises by 9% and 4% respectively. Comparative experiments using different feature extraction methods show that our model performs similar accuracy with the best of others, but its values of sensitivity, specificity and AUC increase by 27.83%, 1.31% and 16.21% respectively. Those results suggest that our method is a promising tool for identifying DNA-binding proteins.
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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.