ArticleBMC bioinformatics2016
Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 37 citations in OpenAlex.
- A comparative study of quantum-inspired PSO and EA with their binary variants for heart disease classification.Scientific reports · 2026Article
- Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns.Scientific reports · 2024Article
- A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization.Entropy (Basel, Switzerland) · 2021Review
- Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges.SN computer science · 2020Review
- DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.Journal of computer-aided molecular design · 2019Article
- MPLs-Pred: Predicting Membrane Protein-Ligand Binding Sites Using Hybrid Sequence-Based Features and Ligand-Specific Models.International journal of molecular sciences · 2019Article
- High-Throughput Identification of Mammalian Secreted Proteins Using Species-Specific Scheme and Application to Human Proteome.Molecules (Basel, Switzerland) · 2018Article
- PSFM-DBT: Identifying DNA-Binding Proteins by Combing Position Specific Frequency Matrix and Distance-Bigram Transformation.International journal of molecular sciences · 2017Article
- Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme.BMC bioinformatics · 2017Article
- Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers.Avicenna journal of medical biotechnologyArticle
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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
backgroundDNA-binding proteins (DBPs) play fundamental roles in many biological processes. Therefore, the developing of effective computational tools for identifying DBPs is becoming highly desirable.
resultsIn this study, we proposed an accurate method for the prediction of DBPs. Firstly, we focused on the challenge of improving DBP prediction accuracy with information solely from the sequence. Secondly, we used multiple informative features to encode the protein. These features included evolutionary conservation profile, secondary structure motifs, and physicochemical properties. Thirdly, we introduced a novel improved Binary Firefly Algorithm (BFA) to remove redundant or noisy features as well as select optimal parameters for the classifier. The experimental results of our predictor on two benchmark datasets outperformed many state-of-the-art predictors, which revealed the effectiveness of our method. The promising prediction performance on a new-compiled independent testing dataset from PDB and a large-scale dataset from UniProt proved the good generalization ability of our method. In addition, the BFA forged in this research would be of great potential in practical applications in optimization fields, especially in feature selection problems.
conclusionsA highly accurate method was proposed for the identification of DBPs. A user-friendly web-server named iDbP (identification of DNA-binding Proteins) was constructed and provided for academic use.
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