[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121316-en":3,"doc-seo-121316-105":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},121316,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Prediction of Solid Tumors Chemotherapy Efficacy Using Machine Learning Applied to Gene Expression Data - Dissertation","Prediction of solid tumor chemotherapy efficacy leverages machine learning models built on gene expression data to address drug-response variability and the problem of drug resistance. The dissertation frames cancer chemotherapy as a decision-support challenge where molecular profiles can help identify suitable therapies for colorectal cancer and extend similar approaches to additional tumor types. Research goals emphasize high-accuracy prediction, methodological rigor, and practical relevance for biomedical oncology.","PREDICTION OF SOLID TUMORS CHEMOTHERAPY EFFICACY USING MACHINE LEARNING APPLIED TO GENE EXPRESSION DATA  \nby  \nSoukaina Amniouel  \nA Dissertation  \nSubmitted to the  \nGraduate Faculty  \nof  \nGeorge Mason University  \nin Partial Fulfillment of  \nThe Requirements for the Degree  \nof  \nDoctor of Philosophy  \nBioinformatics and Computational Biology  \nCommittee:  \n   Dr. M. Saleet Jafri, Committee Chair    Dr. Iosif Vaisman, Committee Member    Dr. Ancha Baranova, Committee Member  \n   Dr. Iosif Vaisman, Director, School of  \nSystems Biology  \n   Dr. Gerald L. R. Weatherspoon, Associate  \nDean for Undergraduate and Graduate Affairs, College of Science  \n   Dr. Fernando R. Miralles-Wilhelm, Dean,  \nCollege of Science  \nDate:   Summer Semester 2024  \nGeorge Mason University  \nFairfax, VA  \nPrediction of Solid Tumors Chemotherapy Efficacy Using Machine Learning Applied to  \nGene Expression Data  \nA Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at George Mason University  \nby  \nSoukaina Amniouel  \nMaster of Science  \nGeorge Mason University, 2020  \nMaster of Science  \nAbdelmalek Essaadi University, 2016  \nBachelor of Science  \nAbdelmalek Essaadi University, 2014  \nDirector: M. Saleet Jafri, Professor  \nSchool of Systems Biology  \nSummer Semester 2024  \nGeorge Mason University  \nFairfax, VA  \nCopyright 2024 Soukaina Amniouel All Rights Reserved  \nDEDICATION  \nThis dissertation is dedicated to my parents, my brother, and my home country.  \nACKNOWLEDGEMENTS  \nI would like to express my deepest gratitude to my parents for their constant support, love, and encouragement throughout different steps of my life including my doctoral studies. Their sacrifice and belief in me have been the foundation of my academic success.  \nMy heartfelt thanks go to my brother for his constant encouragement and understanding. His support has been a source of motivation during challenging times, and I am grateful for the bond we share.  \nI extend my appreciation to my academic advisor and chair, Dr. Mohsin Saleet Jafri, for his invaluable guidance, mentorship, and constructive feedback. His expertise has played a significant role in shaping the direction of my research and academic pursuits.  \nI am also indebted to the members of my thesis committee, Dr. Iosif Vaisman and Dr. Ancha Baranova, for their insightful contributions and thoughtful advice. Their collective expertise has enriched the quality of my academic journey and my research.  \nSpecial thanks are due to my friends who have been a source of support, encouragement, and camaraderie. Your friendship has added joy and balance to my academic journey.  \nFinally, I want to express my gratitude to all those who have played a role, no matter how small, in shaping my academic and personal growth. Your contributions have not gone unnoticed, and I am thankful for the shared experiences and lessons learned.  \nThis thesis is not just a reflection of my individual efforts but a product of the collective support and encouragement I have received. Thank you to each person who has been a part of this transformative journey.  \n.  \nTABLE OF CONTENTS  \nPage  \nList of Tables ...................................................................................................................... x  \nList of Figures .................................................................................................................... xi  \nAbstract ............................................................................................................................ xiii  \nChapter One : Introduction ................................................................................................. 1  \nDrug Resistance in Solid Tumors.................................................................................... 1  \nOverview of Solid Tumors .......................................................................................... 2  \nDrug Response Variability ......","cbCaicQOb5rTwvtK","https://ap.wps.com/l/cbCaicQOb5rTwvtK","pdf",6564218,1,263,"English","en",105,"# List of Tables\n# List of Figures\n# Abstract\n# Chapter One: Introduction\n## Drug Resistance in Solid Tumors\n## Overview of Solid Tumors\n## Drug Response Variability\n## The Role of Gene Expression Data in Cancer Research\n## Basic of Gene Expression\n## Gene Expression and Drug Response\n## Machine Learning in Biomedical Research\n## Introduction to Machine Learning\n## Machine Learning in Oncology\n## Challenges of Using Machine Learning in Drug Response\n## Specific Aims and Goals\n## Specific Aim 1: Colorectal cancer drug selection using gene expression\n## Specific Aim 2: Applying approaches to another cancer type\n## Significance and Innovation\n# Chapter Two: High Accuracy Prediction of Colorectal Cancer Chemotherapy Efficacy using Machine Learning Applied to Gene Expression Data","[{\"question\":\"What problem does the dissertation target in solid tumors?\",\"answer\":\"It focuses on drug resistance and variability in chemotherapy response, aiming to improve prediction of which treatments may work for solid tumors.\"},{\"question\":\"What data type is used for the prediction models?\",\"answer\":\"The work applies machine learning to gene expression data to infer chemotherapy efficacy.\"},{\"question\":\"How do the study’s aims extend beyond colorectal cancer?\",\"answer\":\"One aim evaluates drug selection for colorectal cancer, and a second aim tests whether the same approaches can be applied to another cancer type.\"}]","Prediction of Solid Tumors Chemotherapy Efficacy Using Machine Learning Applied to Gene Expression Data - 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