ArticleComputational and mathematical methods in medicine2020
HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.Scientific reports · 2024Article
- Key risk factors of generalized anxiety disorder in adolescents: machine learning study.Frontiers in public health · 2024Article
- Human Activity Recognition with an HMM-Based Generative Model.Sensors (Basel, Switzerland) · 2023Article
- SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors.Sensors (Basel, Switzerland) · 2022Article
- Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method.Life (Basel, Switzerland) · 2022Article
- Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods.BioMed research international · 2022Article
- Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data.Sensors (Basel, Switzerland) · 2021Article
- 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
- Distinguishing Glioblastoma Subtypes by Methylation Signatures.Frontiers in genetics · 2020Article
- Detecting the Multiomics Signatures of Factor-Specific Inflammatory Effects on Airway Smooth Muscles.Frontiers in genetics · 2020Article
- Identifying Robust Microbiota Signatures and Interpretable Rules to Distinguish Cancer Subtypes.Frontiers in molecular biosciences · 2020Article
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Authors and funding
5 authors.
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Abstract
Prediction of DNA-binding proteins (DBPs) has become a popular research topic in protein science due to its crucial role in all aspects of biological activities. Even though considerable efforts have been devoted to developing powerful computational methods to solve this problem, it is still a challenging task in the field of bioinformatics. A hidden Markov model (HMM) profile has been proved to provide important clues for improving the prediction performance of DBPs. In this paper, we propose a method, called HMMPred, which extracts the features of amino acid composition and auto- and cross-covariance transformation from the HMM profiles, to help train a machine learning model for identification of DBPs. Then, a feature selection technique is performed based on the extreme gradient boosting (XGBoost) algorithm. Finally, the selected optimal features are fed into a support vector machine (SVM) classifier to predict DBPs. The experimental results tested on two benchmark datasets show that the proposed method is superior to most of the existing methods and could serve as an alternative tool to identify DBPs.
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