[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116940-en":3,"doc-seo-116940-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116940,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Detecting Pathobiomes Using Machine Learning - Honors Thesis","Machine learning supports interpreting large-scale data streams, including DNA-related information. This research uses machine learning to detect pathobiomes indicative of salmonella pork, addressing classification challenges caused by the similarity between salmonella-associated pathobiomes and others when using short-read sequencing platforms. Experiments analyze sequences retrieved from the NCBI database to determine what data supports accurate prediction. The work aligns with an existing Walmart Foundation initiative focused on improving pork supply chain food safety.","University of Arkansas, Fayetteville  \nScholarWorks@UARK  \n\n| Industrial Engineering Undergraduate Honors Theses | Industrial Engineering |\n| --- | --- |\n| 5-2023\u003Cbr>Detecting Pathobiomes Using Machine Learning Valerie Jackson\u003Cbr>Valerie Jackson\u003Cbr>Follow this and additional works at: [https://scholarworks.uark.edu/ineguht](https://scholarworks.uark.edu/ineguht)\u003Cbr> Part of the Computational Engineering Commons, Genetic Structures Commons, and the Industrial Engineering Commons |  |\n\nCitation  \nJackson, V., & Jackson, V. (2023) . Detecting Pathobiomes Using Machine Learning. Industrial Engineering Undergraduate Honors Theses Retrieved from [https://scholarworks.uark.edu/ineguht/91](https://scholarworks.uark.edu/ineguht/91)  \n[This Thesis is brought to you for free and open access by the Industrial Engineering at ScholarWorks@UARK. It has](This Thesis is brought to you for free and open access by the Industrial Engineering at ScholarWorks@UARK. It has)[ ](This Thesis is brought to you for free and open access by the Industrial Engineering at ScholarWorks@UARK. It has)been accepted for inclusion in Industrial Engineering Undergraduate Honors Theses by an authorized administrator of ScholarWorks@UARK. For more information, please contact [scholar@uark.edu](scholar@uark.edu).  \nDETECTING PATHOBIOMES USING MACHINE LEARNING  \nDETECTING PATHOBIOMES USING MACHINE LEARNING  \nA thesis submitted in partial fulfillment  \nof the requirements for the degree of Honors Bachelor of Science in Industrial Engineering  \nBy  \nValerie Jackson  \nUniversity of Arkansas  \nBachelor of Science in Industrial Engineering, 2023  \nApril 2023  \nUniversity of Arkansas  \nThis thesis is approved for recommendation to the Graduate Council  \nThesis Director: Dr. Chase Rainwater  \nCommittee member: Dr. John L. Kent  \nAbstract  \nMachine learning is a field with high growth potential due to the overall continuous progressions, developments, advancements, and improvements caused by the way it is used to help interpret and use large amounts of data [1] . One typeof data that can be collected and analyzed by these machine learning models is data that is associated with DNA and information that the DNA gives. The research will be focusing specifically on using machine learning technology to detect pathobiomes indicative of salmonella pork. The pathobiome associated with salmonella is very similar to others, and this causes a problem for classification/detection with short-read sequencing platforms [2] . Because of this, it is important for decision makers to understand what kind of data is needed to help accurately predict these pathobiomes. This research consists of a variety of experiments that help determine what this kind of data is. This is done by reading data taken from various sequences from The National Center for Biotechnology Information(NCBI) database. This project is also being conducted within the backdrop of an existing project from the Walmart foundation project, Improving Food Safety of Pork Supply Chain [3] .  \nTABLE OF CONTENTS  \nAbstract ...................................... ii  \nTable of Contents ................................. iii  \nList of Figures .................................. iv  \nList of Tables ................................... v  \n1 Problem Introduction ............................. 1  \n2 Research Methodology ............................ 3  \n3 Experiments and Analysis .......................... 9  \n4 Conclusion ................................... 13  \nBibliography ................................... 14  \nLIST OF FIGURES  \nFigure 2.1: Sequence from Bioinformatics Toolbox .............. 4  \nFigure 2.2: Code for One-Hot Matrix ..................... 5  \nFigure 2.3: Outputted Matrix ......................... 5  \nFigure 3.1: Results of Overall Experiment ................... 10  \nFigure 3.2: Trial 5 Output ........................... 11  \nFigure 3.3: Results of Batch Size Experiment ................. 12  \nFigure 3.4: Results of Epochs Experi","cbCaifADfTTAKCtR","https://ap.wps.com/l/cbCaifADfTTAKCtR","pdf",309655,1,24,"English","en",105,"# Abstract\n# Table of Contents\n## Problem Introduction\n## Research Methodology\n## Experiments and Analysis\n## Conclusion\n## Bibliography","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis targets the difficulty of accurately detecting salmonella-associated pathobiomes, especially when pathobiomes are similar and short-read sequencing limits classification.\"},{\"question\":\"How is the machine learning work carried out?\",\"answer\":\"It analyzes DNA/RNA sequence data using experiments driven by datasets retrieved from the NCBI database.\"},{\"question\":\"Why is salmonella detection important in the pork supply chain?\",\"answer\":\"Salmonella detection before reaching consumers supports food safety by enabling faster mitigation and tracing, avoiding delays and costs typical of laboratory-based prevalence testing.\"}]","Detecting Pathobiomes Using Machine Learning - 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