[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120271-en":3,"doc-seo-120271-105":30,"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":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},120271,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-driven integration of multimodal data for deciphering breast cancer heterogeneity - PhD thesis","Machine learning-driven integration of multimodal data is presented as an approach to decipher breast cancer (BC) heterogeneity. The work motivates subtype discovery by reviewing clinical, receptor expression, gene expression, and copy number alteration perspectives, then addresses challenges in multi-omics and multimodal analysis. The thesis develops study pipelines including unsupervised clustering, differential gene expression, gene set enrichment, and gene signature creation. It reports subgroup identification in HER2+/ER+ BC and further explores tensor-based multi-omics subtyping to improve computational characterization and validation across datasets.","Machine learning-driven integration of multimodal data for deciphering breast cancer heterogeneity  \nby  \nQian Liu  \nA Thesis submitted to the Faculty of Graduate Studies of The University of Manitoba  \nin partial fulfillment of the requirements of the degree of  \nDOCTOR OF PHILOSOPHY  \nin the  \nIndividual Interdisciplinary Studies Program  \nDepartments of Biochemistry and Medical Genetics, Computer Science, Statistics  \nUniversity of Manitoba  \nWinnipeg  \nCopyright © 2022 by Qian Liu  \nTable of Contents  \nTable of Contents .......................................................................................................................... ii  \n[List of Figures............................................................................................................................... vi](List of Figures............................................................................................................................... vi)  \nList of Tables .............................................................................................................................. viii  \nList of Equations .......................................................................................................................... ix  \nList of Abbreviations .................................................................................................................... x  \nAbstract........................................................................................................................................ xii  \nAcknowledgement ...................................................................................................................... xiv  \nPublications and Contributions ................................................................................................. xv  \n1 Chapter 1: Introduction ....................................................................................................... 1  \n1.1 BC heterogeneity........................................................................................................................ 3  \n1.1.1 BC histopathological staging and grading ............................................................................................. 4  \n1.1.2 Receptor expression-based BC subtyping .............................................................................................. 8  \n1.1.3 Gene expression-based BC subtyping .................................................................................................. 12  \n1.1.4 Copy number alteration-based BC subtyping ...................................................................................... 15  \n1.2 Challenges in deciphering BC heterogeneity......................................................................... 17  \n1.2.1 Substantial heterogeneity in BC subtype ............................................................................................. 17  \n1.2.2 Integration of BC multi-omics data for understanding BC heterogeneity ........................................... 20  \n1.2.3 Complexity in BC multimodal data analyses ....................................................................................... 23  \n1.3 Solutions.................................................................................................................................... 26  \n1.3.1 BC multimodal data ............................................................................................................................. 27  \n1.3.2 Machine learning for understanding BC heterogeneity ....................................................................... 33  \n1.4 Rationale and objectives.......................................................................................................... 36  \n2 Chapter 2: Gene expression based HER2+/ER+ BC heterogeneity study .................... 38  \n2.1 Introduction....................................................................................................","cbCaituvCLvyGF2e","https://ap.wps.com/l/cbCaituvCLvyGF2e","pdf",17803491,1,219,"English","en",105,"# 1 Chapter 1: Introduction\n## 1.1 BC heterogeneity\n## 1.2 Challenges in deciphering BC heterogeneity\n## 1.3 Solutions\n## 1.4 Rationale and objectives\n# 2 Chapter 2: Gene expression based HER2+/ER+ BC heterogeneity study\n## 2.1 Introduction\n## 2.2 Methods\n## 2.3 Results\n## 2.4 Discussion\n## 2.5 Conclusion\n# 3 Chapter 3: Multi-omics-based tensor subtyping for BC","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis focuses on deciphering breast cancer heterogeneity by integrating multimodal data using machine learning approaches.\"},{\"question\":\"Which analysis strategies are used in the HER2+/ER+ study?\",\"answer\":\"It uses unsupervised clustering, differential gene expression analysis, gene set enrichment analysis, and gene signature creation and validation.\"},{\"question\":\"How does the thesis extend beyond gene expression alone?\",\"answer\":\"It introduces multi-omics-based tensor subtyping to computationally characterize BC subgroups and support external validation across datasets.\"}]","Machine learning-driven integration of multimodal data for deciphering breast cancer heterogeneity - PhD thesis | 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