[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123663-en":3,"doc-seo-123663-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123663,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Hierarchical machine learning model predicts antimicrobial peptide activity against Staphylococcus aureus","Hierarchical machine learning model classifies peptides by antimicrobial activity against Staphylococcus aureus, addressing limitations of earlier tools that ignore species-specific effects. A first-level classifier separates antimicrobial peptides (AMPs) from non-AMPs, followed by a second-level model distinguishing AMPs active against S. aureus from those that are not. Using an up-to-date dataset and physicochemical and sequence-derived linguistic features with feature selection, the final system achieves strong predictive performance, including an F1-score of 0.80, recall of 0.86, balanced accuracy of 0.80, and specificity of 0.73, supporting practical peptide-library screening.","TYPE Original Research PUBLISHED 18 September 2023 DOI 10.3389/fmolb.2023.1238509  \nOPEN ACCESS  \nEDITED BY  \nCigdem Sevim Bayrak,  \nIcahn School of Medicine at Mount Sinai, United States  \nREVIEWED BY  \nMichał Burdukiewicz, University of Wrocław, Poland Fernando Lobo Palacios, University of La Laguna, Spain Jian Huang,  \nUniversity of Electronic Science and Technology of China, China  \n*CORRESPONDENCE  \nAmir Homayoun Keihan,  [ahkeihan@bmsu.ac.ir](ahkeihan@bmsu.ac.ir)  \nRECEIVED 11 June 2023  \nACCEPTED 31 August 2023  \nPUBLISHED 18 September 2023  \nCITATION  \nKhabaz H, Rahimi-Nasrabadi M and Keihan AH (2023), Hierarchical machine learning model predicts antimicrobial peptide activity against Staphylococcus aureus.  \nFront. Mol. Biosci. 10:1238509 .  \ndoi: 10.3389/fmolb.2023.1238509  \nCOPYRIGHT  \n© 2023 Khabaz, Rahimi-Nasrabadi and Keihan. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) 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.  \nHierarchical machine learning model predicts antimicrobial peptide activity against Staphylococcus aureus  \nHosein Khabaz 1, Mehdi Rahimi-Nasrabadi 1,2 and Amir Homayoun Keihan 1*  \n1Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran, 2Faculty of Pharmacy, Baqiyatallah University of Medical Sciences, Tehran, Iran  \nIntroduction: Staphylococcus aureus is a dangerous pathogen which causes avast selection of infections. Antimicrobial peptides have been demonstrated as anew hope for developing antibiotic agents against multi-drug-resistant bacteria such as S. aureus. Yet, most studies on developing classification tools for antimicrobial peptide activities do not focus on any specific species, and therefore, their applications are limited.  \nMethods: Here, by using an up-to-date dataset, we have developed a hierarchical machine learning model for classifying peptides with antimicrobial activity against S. aureus. The first-level model classifies peptides into AMPs and non-AMPs. The second-level model classifies AMPs into those active against S. aureus and those not active against this species.  \nResults: Results from both classifiers demonstrate the effectiveness of the hierarchical approach. A comprehensive set of physicochemical and linguisticbased features has been used, and after feature selection steps, only some physicochemical properties were selected. The final model showed the F1-score of 0.80, recall of 0.86, balanced accuracy of 0.80, and specificity of 0.73 on the test set.  \nDiscussion: The susceptibility to a single AMP is highly varied among different target species. Therefore, it cannot be concluded that AMP candidates suggested by AMP/non-AMP classifiers are able to show suitable activity against a specific species. Here, we addressed this issue by creating a hierarchical machine learning model which can be used in practical applications for extracting potential antimicrobial peptides against S. aureus from peptide libraries.  \nKEYWORDS  \nStaphylococcus aureus, antimicrobial peptides, machine learning, antimicrobial activity, classification model  \n1 Introduction  \nStaphylococcus aureus is a prominent human pathogen that causes a wide range of infections, including pleuropulmonary, osteoarticular, skin, and soft tissue infections. In the United States, nearly 50 percent of deaths caused by antibiotic-resistant bacterial pathogens are attributed to methicillin-resistant S. aureus (MRSA) infections  \nFrontiers in Molecular Biosciences 01 [frontiersin.org](frontiersin.org)  \n(Stryjewski and Chambers, 2008; Mohamed et al., 2016) . It has been reported that S. aureus ","cbCainCKTv8GQnuH","https://ap.wps.com/l/cbCainCKTv8GQnuH","pdf",1152082,1,"English","en",105,"# Introduction\n## Antimicrobial peptides and resistance relevance\n## Prior computational approaches and limitations\n# Methods\n# Results\n# Discussion","[{\"question\":\"How does the hierarchical model work for predicting antimicrobial peptide activity?\",\"answer\":\"It uses a two-level design: the first classifier separates AMPs from non-AMPs, and the second classifier determines which AMPs are active against Staphylococcus aureus.\"},{\"question\":\"What features does the model use and what happens after feature selection?\",\"answer\":\"The model combines physicochemical features and linguistic-based features derived from the peptide. After feature selection, only a subset of physicochemical properties remains in the final model.\"},{\"question\":\"What performance metrics were reported on the test set?\",\"answer\":\"The final model reports F1-score 0.80, recall 0.86, balanced accuracy 0.80, and specificity 0.73 on the test set.\"}]","Hierarchical machine learning model predicts antimicrobial peptide activity against Staphylococcus aureus | PDF",1785817891,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"hierarchical-machine-learning-model-predicts-antimicrobial-peptide-activity-against-staphylococcus-aureus","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/hierarchical-machine-learning-model-predicts-antimicrobial-peptide-activity-against-staphylococcus-aureus/123663/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How does the hierarchical model work for predicting antimicrobial peptide activity?","Question",{"text":74,"@type":75},"It uses a two-level design: the first classifier separates AMPs from non-AMPs, and the second classifier determines which AMPs are active against Staphylococcus aureus.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What features does the model use and what happens after feature selection?",{"text":79,"@type":75},"The model combines physicochemical features and linguistic-based features derived from the peptide. 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