[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121811-en":3,"doc-seo-121811-105":30,"detail-sidebar-cat-0-en-105":83},{"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},121811,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Proteomics and Machine Learning for Pulmonary Embolism Risk with Protein Markers","This thesis investigates protein markers associated with pulmonary embolism risk through proteomics and statistical analysis, integrating unsupervised and supervised machine learning methods. It evaluates existing datasets, extracts significant features, and examines gender-related differences using MANOVA. Principal Component Analysis reduces variables from 378 to 59, while Random Forest achieves about 70% classification accuracy. The study applies multivariate tests to support gender findings and highlights proteomics as a basis for diagnostic biomarker development and further clinical research.","East Tennessee State University  \nDigital Commons @ East Tennessee State University  \n\n| Electronic Theses and Dissertations | Student Works |\n| --- | --- |\n| 12-2023\u003Cbr>Proteomics and Machine Learning for Pulmonary Embolism Risk with Protein Markers\u003Cbr>Yaa Amankwah Awuah\u003Cbr>East Tennessee State University\u003Cbr>Follow this and additional works at: [https://dc.etsu.edu/etd](https://dc.etsu.edu/etd)\u003Cbr> Part of the Applied Statistics Commons, Biostatistics Commons, and the Statistical Models Commons |  |\n\nRecommended Citation  \nAwuah, Yaa Amankwah, \"Proteomics and Machine Learning for Pulmonary Embolism Risk with Protein Markers\" (2023) . Electronic Theses and Dissertations. Paper 4327. [https://dc.etsu.edu/etd/4327](https://dc.etsu.edu/etd/4327)  \n[This Thesis-embargo is brought to you for free and open access by the Student Works at Digital Commons @ East](This Thesis-embargo is brought to you for free and open access by the Student Works at Digital Commons @ East)[ ](This Thesis-embargo is brought to you for free and open access by the Student Works at Digital Commons @ East)[Tennessee State University. It has been accepted for inclusion in Electronic Theses and Dissertations by an](Tennessee State University. It has been accepted for inclusion in Electronic Theses and Dissertations by an)[ ](Tennessee State University. It has been accepted for inclusion in Electronic Theses and Dissertations by an)[authorized administrator of Digital Commons @ East Tennessee State University. For more information](authorized administrator of Digital Commons @ East Tennessee State University. For more information), please [contact digilib@etsu.edu](contact digilib@etsu.edu).  \nProteomics and Machine Learning for Pulmonary Embolism Risk with Protein  \nMarkers  \nA thesis  \npresented to  \nthe faculty of the Department of Mathematics and Statistics East Tennessee State University  \nIn partial fulfillment of the requirements for the degree  \nMaster of Science in Mathematical Sciences  \nby Yaa Amankwah Awuah  \nDecember 2023  \nMostafa Zahed, Ph.D., Chair  \nRobert M. Price, Ph.D.  \nJean-Marie Hendrickson, Ph.D.  \nKeywords: Proteomics, Dimension Reduction, Random Forest, Features Extraction, MANOVA, Lawley-Hotelling’s , Pillai’s Test, Wilk’s Lambda, Roy’s Largest Root  \nABSTRACT  \nProteomics and Machine Learning for Pulmonary Embolism Risk with Protein  \nMarkers  \nby  \nYaa Amankwah Awuah  \nThis thesis investigates protein markers linked to pulmonary embolism risk using proteomics and statistical methods, employing unsupervised and supervised machine learning techniques. The research analyzes existing datasets, identifies significant features, and observes gender differences through MANOVA. Principal Component Analysis reduces variables from 378 to 59, and Random Forest achieves 70% accuracy. These findings contribute to our understanding of pulmonary embolism and may lead to diagnostic biomarkers. MANOVA reveals significant gender differences, and applying proteomics holds promise for clinical practice and research.  \nCopyright 2023 by Yaa Amankwah Awuah All Rights Reserved  \n3  \nACKNOWLEDGMENTS  \nI extend my heartfelt gratitude to my thesis supervisor, Dr. Mostafa Zahed, for his exceptional guidance, unwavering support, and valuable recommendations that played an integral role in navigating this journey and achieving timely milestones. My sincere appreciation goes to my esteemed committee members, Dr. Bob Price, and Dr. JeanMarie Hendrickson, for their insightful contributions to this endeavor. I am indebted to the Department of Mathematics and Statistics at East Tennessee State University for their financial assistance that enabled the successful completion of this project and my graduate studies. I also extend profound thanks to my family and friends for their constant encouragement and steadfast support.  \nTABLE OF CONTENTS  \nABSTRACT .................................. 2  \nACKNOWLEDGMENTS ........................... 4  \nLIST OF TABLES ..............","cbCaiohwFgZ1TgKS","https://ap.wps.com/l/cbCaiohwFgZ1TgKS","pdf",4301074,1,91,"English","en",105,"# Abstract\n# Acknowledgments\n# List of Tables\n# List of Figures\n# Introduction\n## Terminologies and Definitions\n## Background\n# Previous Study\n# Research and Methodology\n## Pulmonary Embolism\n## Data Description\n## Techniques Involved in Proteomic Profiling\n## Supervised and Unsupervised Techniques\n## Dimension Reduction Techniques\n## Supervised Machine Learning Technique\n## Random Forest\n## Statistical Techniques Used\n## Multivariate Analysis of Variance (MANOVA)\n## Assumptions of MANOVA\n## Wilk’s Lambda (Λ)\n## Pillai’s Trace\n## Hotelling-Lawley Trace\n## Roy’s Largest Root\n# Analysis of Results\n## Introduction\n## Data Cleaning and Exploration\n## Methodology Flowchart: Variable Reduction and Analysis Techniques\n## Principal Component Analysis (PCA)\n## Random Forest Classification\n## Multivariate Analysis of Variance (MANOVA)","[{\"question\":\"How does the thesis assess gender differences?\",\"answer\":\"Gender differences are evaluated using MANOVA, with multivariate test statistics including Wilk’s Lambda, Pillai’s Trace, Hotelling-Lawley Trace, and Roy’s Largest Root.\"}]","Proteomics and Machine Learning for Pulmonary Embolism Risk with Protein Markers | PDF",1785806989,229,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"proteomics-and-machine-learning-for-pulmonary-embolism-risk-with-protein-markers","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/proteomics-and-machine-learning-for-pulmonary-embolism-risk-with-protein-markers/121811/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How does the thesis assess gender differences?","Question",{"text":75,"@type":76},"Gender differences are evaluated using MANOVA, with multivariate test statistics including Wilk’s Lambda, Pillai’s Trace, Hotelling-Lawley Trace, and Roy’s Largest Root.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]