ReviewFrontiers in pharmacology2021
DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins.
Review in Frontiers in pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- DrugProtAI: A machine learning-driven approach for predicting protein druggability through feature engineering and robust partition-based ensemble methods.Briefings in bioinformatics · 2025Article
- Classification of Acid and Alkaline Enzymes Based on Normalized Van der Waals Volume Features.Proteomics. Clinical applications · 2025Article
- iAmyP: A Multi-view Learning for Amyloidogenic Hexapeptides Identification Based on Sequence Least Squares Programming.Interdisciplinary sciences, computational life sciences · 2025Article
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- Repurposing FDA-Approved Drugs Against Potential Drug Targets Involved in Brain Inflammation Contributing to Alzheimer's Disease.Targets (Basel) · 2024Article
- DPI_CDF: druggable protein identifier using cascade deep forest.BMC bioinformatics · 2024Article
- PINNED: identifying characteristics of druggable human proteins using an interpretable neural network.Journal of cheminformatics · 2023Article
- Toxic mechanism of the Mongolian medicine "Hunqile-7" based on metabonomics and the metabolism of intestinal flora.Toxicology research · 2023Article
- Empirical comparison and analysis of machine learning-based approaches for druggable protein identification.EXCLI journal · 2023Review
- Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework.iScience · 2022Article
- Antimicrobial Peptides Prediction method based on sequence multidimensional feature embedding.Frontiers in genetics · 2022Article
- A method for identifying moonlighting proteins based on linear discriminant analysis and bagging-SVM.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.
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Registered trials
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 Socratic graph.