[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116914-en":3,"doc-seo-116914-105":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},116914,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Interpretable Deep Neural Networks for More Accurate Predictive Genomics and Genome-Wide Association Studies - A Dissertation","The dissertation presents interpretable deep neural network approaches to improve predictive genomics, with a focus on genome-wide association studies and polygenic risk score estimation. It develops deep learning and multi-task learning models for breast cancer and expands interpretability beyond first-order feature attribution to structured, second-order explanations. An additional linearizing neural network architecture, LINA, is introduced to enable more accurate first- and second-order interpretations, supported by benchmarking on real-world and synthetic datasets. Comparative analyses evaluate estimation quality against statistical and alternative machine learning baselines.","UNIVERSITY OF OKLAHOMA  \nGRADUATE COLLEGE  \nINTERPRETABLE DEEP NEURAL NETWORKS FOR MORE ACCURATE PREDICTIVE GENOMICS AND GENOME-WIDE ASSOCIATION STUDIES.  \nA DISSERTATION  \nSUBMITTED TO THE GRADUATE FACULTY in partial fulfillment of the requirements for the Degree of  \nDOCTOR OF PHILOSOPHY  \nBy  \nADRIEN BADR´E  \nNorman, Oklahoma  \n2023  \nINTERPRETABLE DEEP NEURAL NETWORKS FOR MORE ACCURATE PREDICTIVE GENOMICS AND GENOME-WIDE ASSOCIATION STUDIES.  \nA DISSERTATION APPROVED FOR THE  \nSCHOOL OF COMPUTER SCIENCE  \nBY THE COMMITTEE CONSISTING OF  \nDr. Chongle Pan (Chair)  \nDr. Dean Hougen  \nDr. Qi Cheng  \nDr. Henry Neeman  \nDr. Krithivasan Sankaranarayanan  \n© Copyright by ADRIEN BADR´E 2023 All Rights Reserved.  \nAcknowledgments  \nI want to thank Dr. Chongle Pan for being my committee chair, advisor, coauthor, mentor, and friend. His support, specifically during the pandemic, and tireless persistence helped me realize this work. None of my achievements would have been possible without him.  \nI would also like to thank my wife and the love of my life, Charissa, for her endless love and support through this journey. Without her, I would have collapsed many times. I also want to thank my parents, J´erˆome and Sophie, my sisters, Aurore and Flore, and my family for their continuous support.  \nNext, I would like to thank Virginie Perez-Woods. She was an amazingly helpful academic coordinator. More than being a great friend, she was a french presence and launched my dissertation journey in the best possible way. I also want to address my gratitude to all the current staff at the CS department that helped me and guided me during this fantastic experience. Then, my gratitude goes to all my labmates, and I wish them the best for their Ph.D. journey.  \nFinally, I would like to thank the professors (Dr. Cheng, Dr. Hougen, Dr. Neemanand Dr. Sankaranarayanan) on my committee for their time and advice.  \nTable of Contents  \nchapterAcknowledgmentsivsection*.1  \nList Of Tables viii  \nList Of Figures ix  \nAbstract xiii  \n1 Introduction 1  \n1.1 Machine Learning (ML) .......................... 1  \n1.1.1 Types of Machine Learning .................... 1  \n1.1.1.1 Supervised Learning ................... 1  \n1.1.1.2 Unsupervised Learning .................. 3  \n1.1.2 Regression vs Classification .................... 3  \n1.1.3 Interpretable Machine Learning .................. 6  \n1.2 Supervised Machine Learning Algorithms ................. 8  \n1.2.1 Linear Regression .......................... 8  \n1.2.2 Logistic Regression ......................... 9  \n1.2.3 Deep Learning ............................ 10  \n1.2.3.1 Artificial Neural Network ................ 10  \n1.2.3.2 Multi-task learning .................... 18  \n1.3 Genomics .................................. 18  \n1.4 Genome-Wide Association Studies ..................... 22  \n1.5 Conclusion .................................. 23  \n2 Deep neural network improves the estimation of polygenic risk scores for breast cancer 25  \n2.1 Introduction ................................. 26  \n2.2 Methods ................................... 28  \n2.2.1 Breast cancer GWAS data ..................... 28  \n2.2.2 Development of deep neural network models for PRS estimation 30  \n2.2.3 Development of alternative machine learning models for PRS estimation ............................... 31  \n2.2.4 Development of statistical models for PRS estimation ...... 32  \n2.2.5 DNN model interpretation protocol ................ 33  \n2.3 Results and Discussion ........................... 33  \n2.3.1 Development of a machine learning model for breast cancer PRS estimation .............................. 34  \n2.3.2 Comparison of the DNN model with statistical models for breast cancer PRS estimation ....................... 40  \n2.3.3 Interpretation of the DNN model ................. 47  \n3 LINA: A Linearizing Neural Network Architecture for Accurate FirstOrder and Second-Order Interpretations 54  \n3.1 Introduction .................................","cbCaillTnUZAT5rN","https://ap.wps.com/l/cbCaillTnUZAT5rN","pdf",7774504,1,149,"English","en",105,"# Acknowledgments\n# List of Tables\n# List of Figures\n# Abstract\n# Introduction\n## Machine Learning (ML)\n## Interpretable Machine Learning\n## Supervised Machine Learning Algorithms\n## Genomics\n## Genome-Wide Association Studies\n# Deep neural network improves the estimation of polygenic risk scores for breast cancer\n## Methods\n## Results and Discussion\n# LINA: A Linearizing Neural Network Architecture for Accurate First-Order and Second-Order Interpretations\n## Methods\n## Data and Experimental Setup\n## Results and Discussion\n# Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis\n## Methods\n## Results","[{\"question\":\"What problem does the dissertation address in predictive genomics?\",\"answer\":\"It targets more accurate estimation of polygenic risk scores using genome-wide association studies, while making deep models interpretable. The work aims to connect predictive performance with explanation mechanisms.\"},{\"question\":\"How does the dissertation improve interpretability beyond standard feature attribution?\",\"answer\":\"It introduces a linearizing neural network architecture (LINA) designed for accurate first-order and second-order interpretations. The protocol supports both instance-wise and model-wise interpretations.\"},{\"question\":\"What models are evaluated for polygenic risk score estimation?\",\"answer\":\"Deep neural network models, alternative machine learning and statistical baselines, and explainable multi-task learning models are compared. Breast cancer-focused experiments and many-disease parallel estimation are both discussed.\"}]","Interpretable Deep Neural Networks for More Accurate Predictive Genomics and Genome-Wide Association Studies - A Dissertation | PDF",1785672502,375,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"interpretable-deep-neural-networks-for-more-accurate-predictive-genomics-and-genome-wide-association-studies-a-dissertation","",{"@graph":36,"@context":85},[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/interpretable-deep-neural-networks-for-more-accurate-predictive-genomics-and-genome-wide-association-studies-a-dissertation/116914/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the dissertation address in predictive genomics?","Question",{"text":75,"@type":76},"It targets more accurate estimation of polygenic risk scores using genome-wide association studies, while making deep models interpretable. The work aims to connect predictive performance with explanation mechanisms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation improve interpretability beyond standard feature attribution?",{"text":80,"@type":76},"It introduces a linearizing neural network architecture (LINA) designed for accurate first-order and second-order interpretations. The protocol supports both instance-wise and model-wise interpretations.",{"name":82,"@type":73,"acceptedAnswer":83},"What models are evaluated for polygenic risk score estimation?",{"text":84,"@type":76},"Deep neural network models, alternative machine learning and statistical baselines, and explainable multi-task learning models are compared. Breast cancer-focused experiments and many-disease parallel estimation are both discussed.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]