TECHNOLOGY AND CODE article
Front. Bioinform.
Sec. Protein Bioinformatics
This article is part of the Research TopicWomen in Bioinformatics 2025View all articles
Bacteriocin Prediction Through Cross-Validation-Based and Hypergraph-Based Feature Evaluation Approaches
Provisionally accepted
- 1Washington State University, Pullman, United States
- 2Emporia State University School of Business and Technology, Emporia, United States
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Bacteriocins offer a promising solution to antibiotic resistance, possessing the ability to target a wide range of bacteria with precision. Thus, there is an urgent need for a computational model to predict new bacteriocins and aid in drug development. This work centers on constructing web-based predictive models using the XGBoost machine learning algorithm, based on the physicochemical properties, structural characteristics, and sequence profiles of protein sequences. We employed correlation analyses, cross-validation, and hypergraph-based techniques to select features. Cross-validated feature selection (CVFS) partitions the dataset, selects features within each partition, and identifies common features, ensuring representativeness. On the contrary, hypergraph-based feature evaluation (HFE) focuses on minimizing hypergraph cut conductance, leveraging higher-order data relationships to precisely utilize information regarding feature and sample correlations. The XGBoost models were built using the selected features obtained from these two feature evaluation methods. We also analyzed the feature contributions directly from the best model using SHapley Additive exPlanations (SHAP). Our HFE-based approach achieved 99.11% accuracy and an AUC of 0.9974 on the test data, overall outperforming the CVFS-based feature evaluation method and yielding results comparable to existing approaches. The most influential features are related to solvent accessibility for buried residues, followed by the composition of cysteine. Our web application, accessible at https://shiny.tricities.wsu.edu/bacteriocin-prediction/, offers prediction results, probability scores, and SHAP plots using both cross-validation-and hypergraph-based methods, along with previously implemented approaches for feature selection.
Keywords: antimicrobial resistance, antimicrobial peptides, bacteriocin prediction, Feature Selection, machine learning, Shapley additive explanations, web application
Received: 27 Aug 2025; Accepted: 27 Oct 2025.
Copyright: © 2025 Akhter and Miller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Suraiya Akhter, sakhter1@emporia.edu
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