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This dissertation develops machine learning-based software and a web application for precisely detecting bacteriocin protein sequences. Features are extracted from known bacteriocins and non-bacteriocins across physicochemical, structural, and sequence-profile attributes, then refined through multiple feature evaluation methods. The best model reaches 99.11% accuracy and 0.9984 AUC on testing data, with Shapley-value analysis to interpret feature contributions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-based-prediction-of-bacteriocins-via-feature-evaluation-doctoral-dissertation/290715/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-based-prediction-of-bacteriocins-via-feature-evaluation-doctoral-dissertation/290715.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the dissertation address?","Question",{"text":112,"@type":113},"It tackles antibiotic resistance by developing an accurate and efficient computational approach to predict novel bacteriocins.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the machine learning features selected and refined?",{"text":117,"@type":113},"The study extracted potential features from known bacteriocins and non-bacteriocins, then refined the feature set using multiple evaluation methods including recursive feature elimination, genetic algorithms, alternating decision trees, cross-validated methods, and hypergraph-based techniques.",{"name":119,"@type":110,"acceptedAnswer":120},"What performance and interpretability results are reported?",{"text":121,"@type":113},"The best predictive model achieves 99.11% accuracy and 0.9984 AUC on the testing dataset. Feature contributions are analyzed directly using Shapley values.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290715,1789642426,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","MACHINE LEARNING-BASED PREDICTION OF BACTERIOCINS  \nVIA FEATURE EVALUATION  \nBy  \nSURAIYA AKHTER  \nA dissertation submitted in partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nWASHINGTON STATE UNIVERSITY School of Electrical Engineering and Computer Science  \nMAY 2024  \n© Copyright by SURAIYA AKHTER, 2024 All Rights Reserved  \n© Copyright by SURAIYA AKHTER, 2024 All Rights Reserved  \nTo the Faculty of Washington State University:  \nThe members of the Committee appointed to examine the dissertation of SURAIYAAKHTER find it satisfactory and recommend that it be accepted.  \nJohn H. Miller, Ph.D. , Chair  \nShira L. Broschat, Ph.D.  \nMohamed A. Osman, Ph.D.  \nACKNOWLEDGMENT  \nI want to express my profound gratitude to my advisor, Dr. John H. Miller, for his guidance and all the support he provided to accomplish my Ph.D. research work. I am grateful to my committee members, Dr. Shira L. Broschat and Dr. Mohamed A. Osman, for their insightful comments and suggestions during my preliminary and final examinations, which greatly helped improve the contents of this dissertation. I thank David Search from the Information Technology Department at Washington State University Tri-Cities for assisting in deploying my Ph.D. research-based web application to the university server. I am highly thankful to Dr. Changki Mo from the School of Engineering and Applied Sciences at Washington State University Tri-Cities for helping me get financial aid from the university to publish my Ph.D. research papers in university-listed journals. I wholeheartedly appreciate my husband, Dr. Abu Sayed Chowdhury, for his consistent support and belief that I can finish my Ph.D. Finally, I am indebted to my parents and sisters for their unconditional love and encouragement [during my Ph.D. study](during my Ph.D. study).  \nMACHINE LEARNING-BASED PREDICTION OF BACTERIOCINS  \nVIA FEATURE EVALUATION  \nAbstract  \nby Suraiya Akhter, Ph.D.  \nWashington State University  \nMay 2024  \nChair: John H. Miller  \nAntibiotic resistance is a major public health concern around the globe. As a result, researchers are always looking for new compounds to develop new antibiotic drugs to combat antibiotic-resistant bacteria. The use of bacteriocins has emerged as a promising strategy in the development of new drugs to combat antibiotic resistance, given their ability to kill bacteria with both broad and narrow natural spectra. Therefore, there is a strong need for an accurate and efficient computational model to predict novel bacteriocins. Machine learning’s ability to learn patterns and features from bacteriocin sequences that are difficult to capture using sequence matching-based methods makes it a potentially superior choice for accurate prediction. Our objective was to develop a machine learning-based software tool and a machine learning-based web application using selected features to precisely detect bacteriocin protein sequences. To achieve this, we initially extracted potential features from known bacteriocins and non-bacteriocins, considering physicochemical, structural, and sequence-profile attributes of the protein sequences. Subsequently, we refined the feature set using various feature evaluation methods, including statistical justifications, recursive feature elimination techniques, alternating decision tree, genetic algorithm, linear support  \nvector classifier, cross-validated, and hypergraph-based techniques. We developed several popular and widely used machine learning models, and our best predictive model achieved an accuracy of 99 . 11% and an AUC of 0 .9984 for the testing dataset. Furthermore, we analyzed the feature contributions directly from the predictive model using the Shapley values. Our software tool offers prediction results based on the best predictive model, using a statistically justified feature set. In contrast, our web application gives prediction results using the best models created with feature sets selected from all","cbCaindGfMeNEvCj","https://ap.wps.com/l/cbCaindGfMeNEvCj","pdf",2736742,205,"English","# Acknowledgment\n# Abstract\n# List of Tables\n# List of Figures\n# Chapters\n## 1 Introduction\n## 2 BAPRES: A Software Tool for Predicting Bacteriocins Using an Optimal Set of Features\n## 3 BPAGS: A Web Application for Bacteriocin Prediction via Feature Evaluation Using Alternating Decision Tree, Genetic Algorithm, and Linear Support Vector Classifier","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It tackles antibiotic resistance by developing an accurate and efficient computational approach to predict novel bacteriocins.\"},{\"question\":\"How were the machine learning features selected and refined?\",\"answer\":\"The study extracted potential features from known bacteriocins and non-bacteriocins, then refined the feature set using multiple evaluation methods including recursive feature elimination, genetic algorithms, alternating decision trees, cross-validated methods, and hypergraph-based techniques.\"},{\"question\":\"What performance and interpretability results are reported?\",\"answer\":\"The best predictive model achieves 99.11% accuracy and 0.9984 AUC on the testing dataset. Feature contributions are analyzed directly using Shapley values.\"}]","MACHINE LEARNING-BASED PREDICTION OF BACTERIOCINS VIA FEATURE EVALUATION - Doctoral Dissertation | PDF",517]