ArticleBioMed research international2017
HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- In silico identification of archaeal DNA-binding proteins.Bioinformatics (Oxford, England) · 2025Article
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- Improved prediction of DNA and RNA binding proteins with deep learning models.Briefings in bioinformatics · 2024Article
- Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.Scientific reports · 2024Article
- COVID-19 Vaccines: Computational tools and Development.Informatics in medicine unlocked · 2023Review
- Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles.Computational and mathematical methods in medicine · 2021Article
- Article
- Computational tools for modern vaccine development.Human vaccines & immunotherapeutics · 2020Review
- 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
- DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.Journal of computer-aided molecular design · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.
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Registered trials
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