ArticleBMC bioinformatics2018
PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- DNAreader: accurate prediction of DNA-binding residues in structured and disordered proteins using transformers and contrastive learning.Nucleic acids research · 2026Article
- TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.Communications biology · 2025Article
- Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.Microbial biotechnology · 2025Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- DRBpred: A sequence-based machine learning method to effectively predict DNA- and RNA-binding residues.Computers in biology and medicine · 2024Article
- PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information.Interdisciplinary sciences, computational life sciences · 2024Article
- HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins.Nucleic acids research · 2024Article
- Development and application of a machine learning-based predictive model for obstructive sleep apnea screening.Frontiers in big data · 2024Article
- Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation.Scientific reports · 2023Article
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- A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level.BioMed research international · 2022Review
- VirVACPRED: A Web Server for Prediction of Protective Viral Antigens.International journal of peptide research and therapeutics · 2022Article
- Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.Briefings in bioinformatics · 2021Article
- DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.International journal of molecular sciences · 2021Article
- Development of machine learning model for diagnostic disease prediction based on laboratory tests.Scientific reports · 2021Article
- Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework.Briefings in bioinformatics · 2021Article
- Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach.Surgical endoscopy · 2020Observational
- Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure.BMC medical informatics and decision making · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying specific residues for protein-DNA interactions are of considerable importance to better recognize the binding mechanism of protein-DNA complexes. Despite the fact that many computational DNA-binding residue prediction approaches have been developed, there is still significant room for improvement concerning overall performance and availability.
resultsHere, we present an efficient approach termed PDRLGB that uses a light gradient boosting machine (LightGBM) to predict binding residues in protein-DNA complexes. Initially, we extract a wide variety of 913 sequence and structure features with a sliding window of 11. Then, we apply the random forest algorithm to sort the features in descending order of importance and obtain the optimal subset of features using incremental feature selection. Based on the selected feature set, we use a light gradient boosting machine to build the prediction model for DNA-binding residues. Our PDRLGB method shows better overall predictive accuracy and relatively less training time than other widely used machine learning (ML) methods such as random forest (RF), Adaboost and support vector machine (SVM). We further compare PDRLGB with various existing approaches on the independent test datasets and show improvement in results over the existing state-of-the-art approaches.
conclusionsPDRLGB is an efficient approach to predict specific residues for protein-DNA interactions.
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Registered trials
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