[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126242-en":3,"doc-seo-126242-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126242,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","SINGLE VESICLE SURFACE PROTEIN PROFILING AND MACHINE LEARNING-ASSISTED DUAL IMAGE ANALYSIS FOR BREAST CANCER DETECTION","The dissertation investigates single extracellular vesicle (EV) surface protein profiling as a biomarker strategy for breast cancer detection. It reviews cancer and screening methods while emphasizing EV surface proteins and their clinical diagnostic relevance. The study integrates machine learning and digital signal processing to improve identification and analysis performance for EV surface proteins. It also incorporates a review-based framework summarizing nanotechnology approaches for molecular EV detection and analysis, organized by nanostructure type, to contextualize current and future diagnostic capabilities.","University of Memphis  \nUniversity of Memphis Digital Commons  \nElectronic Theses and Dissertations  \n5-5-2025  \nSINGLE VESICLE SURFACE PROTEIN PROFILING AND MACHINE LEARNING-ASSISTED DUAL IMAGE ANALYSIS FOR BREAST CANCER DETECTION  \nMitchell Lee Taylor  \nFollow this and additional works at: [https://digitalcommons.memphis.edu/etd](https://digitalcommons.memphis.edu/etd)  \nRecommended Citation  \nTaylor, Mitchell Lee, \"SINGLE VESICLE SURFACE PROTEIN PROFILING AND MACHINE LEARNINGASSISTED DUAL IMAGE ANALYSIS FOR BREAST CANCER DETECTION\" (2025) . Electronic Theses and Dissertations. 3804.  \n[https://digitalcommons.memphis.edu/etd/3804](https://digitalcommons.memphis.edu/etd/3804)  \nThis Dissertation is brought to you for free and open access by University of Memphis Digital Commons. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of University of Memphis Digital Commons. For more information, please contact [khggerty@memphis.edu](khggerty@memphis.edu).  \nSINGLE VESICLE SURFACE PROTEIN PROFILING AND MACHINE LEARNING-ASSISTED DUAL IMAGE ANALYSIS FOR BREAST CANCER DETECTION  \nby  \nMitchell Lee Taylor  \nA Dissertation  \nSubmitted in Partial Fulfillment of the  \nRequirements for the Degree of  \nDoctor of Philosophy  \nMajor: Chemistry  \nThe University of Memphis  \nMay 2025  \nDEDICATION  \nThis dissertation is dedicated to my parents and my brother Ashby, whose support is the reason I’ve been able to complete this arduous academic endeavor. Thank you for the sacrifices you have all made and for the encouragement you offered when the mountain appeared too difficult to traverse. Without the three of you, I would not have been able to reach this monumental achievement. Your unwavering belief in me has been my anchor in moments of doubt, and your love has been my greatest source of strength. I am forever grateful for the foundation you have helped build for me, and I carry your lessons, resilience, and kindness with me always.  \nACKNOWLEDGMENT  \nI want to thank Dr. Xiaohua Huang for welcoming me into her research group during the COVID pandemic. After taking her class, I was certain I wanted to pursue my PhD with her, and she graciously accepted me as a student.  \nThroughout my time in her lab, I have learned not only how to communicate more effectively but also how to tackle experimental challenges and approach science with a comprehensive perspective. Her patience and mentorship have been instrumental in shaping my growth as a scientist and I am deeply grateful for the experiences I had under her guidance. Thankyou for always supporting me and for providing direction both scientifically and professionally.  \nI would like to thank Dr. Gary Emmert for his support and for helping me initiate this journey when I came to him for guidance. Without him, I would not have pursued my PhD.  \nI would also like to thank my committee members, Dr. Yongmei Wang, Dr. Michael Brown, Dr. Qianyi Cheng, Dr. Daniel Nascimento, and Dr. Thang Hoang for their time and support. Without them, this work would have been impossible to complete. I would especially like to thank Dr. Yongmei Wang for her guidance as an unofficial second advisor. The computational skills I learned from her have been essential during my research. Furthermore, I would like to thank Dr. Caleb Gallops for his assistance in my computational pursuits.  \nI am also very grateful to work with my fellow lab members, including Drs. Kristopher Amrhein and Madhusudhan Alle who have collaborated with me with their experimental skills anda number of undergraduate students who I have trained and contributed to the single vesicle project. In addition, I would like to thank Drs. Raymond E. Wilson and Dr. Vojtěch Vinduška,  \nwhose patience and knowledge helped guide me to the right answers and who gave me the confidence I could complete my PhD.  \nLastly, I would like to extend my sincere gratitude to The University of Memphis and the Department of Chem","cbCaihgIPScFTPcN","https://ap.wps.com/l/cbCaihgIPScFTPcN","pdf",7878219,2,1,164,"English","en",105,"# Dedication\n# Acknowledgment\n# Preface\n## Cancer overview and EV biomarker roles\n## Integration of machine learning and digital signal processing\n## Review-based chapter and EV detection techniques","[{\"question\":\"What biomarker approach does the dissertation focus on for breast cancer detection?\",\"answer\":\"It focuses on single extracellular vesicle surface protein profiling, treating surface proteins as biomarkers relevant to cancer detection and diagnosis.\"},{\"question\":\"How does machine learning contribute to the research?\",\"answer\":\"The work integrates machine learning with digital signal processing to enhance identification and analysis of EV surface proteins, improving accuracy and efficiency in cancer detection.\"},{\"question\":\"What is covered in the review-based chapter?\",\"answer\":\"It summarizes nanotechnology-based techniques and devices for molecular EV detection and analysis, especially applications in cancer research and diagnostics, organized by nanostructure type.\"}]","SINGLE VESICLE SURFACE PROTEIN PROFILING AND MACHINE LEARNING-ASSISTED DUAL IMAGE ANALYSIS FOR BREAST CANCER DETECTION | 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