[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127753-en":3,"doc-seo-127753-105":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},127753,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Generalizable machine learning methods for network inference in systems biology","Generalizable machine learning methods for network inference in systems biology investigate how computational models can infer cellular interaction and coordination networks from complex biological measurements. The work includes the development of a dimensionality reduction framework, deciphR, and associated software to support network inference in single-cell genomics data. It also integrates previously published, peer-reviewed single-cell analyses, including hormoneregulated cell-cell interaction mapping, to validate computational approaches across datasets and biological contexts.","UCSF  \nUC San Francisco Electronic Theses and Dissertations  \nTitle  \nGeneralizable machine learning methods for network inference in systems biology  \nPermalink  \n[https://escholarship.org/uc/item/28m080vx](https://escholarship.org/uc/item/28m080vx)  \nAuthor  \nRabadam, Gabrielle  \nPublication Date  \n2024  \nSupplemental Material  \n[https://escholarship.org/uc/item/28m080vx\\#supplemental](https://escholarship.org/uc/item/28m080vx#supplemental)  \n[Peer reviewed|Thesis/dissertation](Peer reviewed|Thesis/dissertation)  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n􀀈􀀙!􀀙$􀀕 􀀝*􀀕􀀖 􀀙 􀀌􀀕􀀗􀀜􀀝!􀀙 􀀋􀀙􀀕$!􀀝!􀀛 􀀌􀀙&􀀜\"􀀘% 􀀚\"$ 􀀍􀀙&(\"$􀀞 􀀊!􀀚􀀙$􀀙!􀀗􀀙 􀀝! 􀀑)%&􀀙 % 􀀃􀀝\" \"􀀛)  \nby  \n􀀈􀀕􀀖$􀀝􀀙 􀀙 􀀐􀀕􀀖􀀕􀀘􀀕  \n􀀅􀀊􀀑􀀑􀀆􀀐􀀒􀀂􀀒􀀊􀀎􀀍  \nSubmitted in partial satisfaction of the requirements for degree of 􀀅􀀎􀀄􀀒􀀎􀀐 􀀎􀀇 􀀏􀀉􀀊􀀋􀀎􀀑􀀎􀀏􀀉􀀓  \nin  \n􀀃􀀝\"􀀙!􀀛􀀝!􀀙􀀙$􀀝!􀀛  \nin the  \nGRADUATE DIVISION  \nof the  \nUNIVERSITY OF CALIFORNIA, SAN FRANCISCO AND  \nUNIVERSITY OF CALIFORNIA, BERKELEY  \nApproved:  \n􀀔􀀙' 􀀈􀀕$&!􀀙$  \n\n| Chair\u003Cbr>􀀊􀀕􀀝! 􀀄 􀀕$􀀞 |\n| --- |\n| 􀀌􀀕$􀀝!􀀕 􀀑􀀝$\"&􀀕 |\n| 􀀌􀀕&&􀀜􀀙( 􀀑\\#􀀝&*􀀙$ |\n|  |\n\nCommittee Members  \nii  \nTo my mother Eleanor Paras Rabadam, who first made science seem possible. And to my father, Luisito Rabadam, who patiently taught me how to count—look, I made it past 10.  \nACKNOWLEDGMENTS  \nFirst and foremost, I want to thank my advisor Dr. Zev Gartner for his support. Among several things, I am grateful that he has continuously challenged me to trust my ambition as a scientist just as much as falling back on my pragmatism as an engineer. Iam by far a better scientist because of Zev’s encouragement to think and dream bigger. Furthermore, I am grateful to Zev for imparting on me the importance of scientific storytelling. It is this capacity for storytelling—the ability to share science with such infectious enthusiasm that people cannot help but want to work with you—that has opened so many doors for me as a member of the Gartner Lab.  \nIn particular, I am grateful to Zev for catalyzing my collaboration with Dr. Jessica Neely and Dr. Marina Sirota. Based on Zev’s vote of confidence, Dr. Neely entrusted me with precious patient data that had taken the Department of Pediatric Rheumatology over a decade to acquire. Throughout this project, it was primarily Dr. Neely’s recognition and respect of my expertise that empowered me to start seeing myself as a computational biologist. I would also like to thank Dr. Sirota for extending the invitation to join her lab meetings where I could receive feedback on multiple iterations of my computational work and learn from her lab’s complementary expertise.  \nAnd of course, I would not be here were it not for the Gartner lab, members past and present. Be it through lab meetings, informal conversations, writing revisions, or preparation for conferences, everyone’s input has refined my scientific thinking. I specifically want to thank Dr. Lyndsay Murrow who mentored me when I first joined the  \nlab and gave me the space to grow into an independent collaborator. I also want to thank  \nDr. Brittany Moser for training me on the MULTIseq protocol and empowering me to do these experiments independently.  \nAt UCSF, I am grateful for Laura Shub, who finally realized after two quarters of classes together as first-years that I was trying to be her friend. What a joy it is to watch your best friend grow into an absolute force to be reckoned with as a scientist, and a privilege to earn her loyalty. I am also indebted to Dr. Scott Nanda who continued to remind me during this hardest year yet,“Just keep pushing. PhD is PhD is PhD.” Thankyou both for stoking the fire and ensuring the flames never blew out completely.  \nI also want to thank my beloved friends across multiple time zones who have continued to cheer me on, check on me, and remind me to take a breath of fresh air: Dustin, Brenna, Lindsay, Selena, Kristina, Emily, and Marina, I owe you all so much. And to my climbing family scattered across the world, thank","cbCaiqeCspCpcgPZ","https://ap.wps.com/l/cbCaiqeCspCpcgPZ","pdf",20635974,1,213,"English","en",105,"# Title and metadata\n## Author and publication information\n## Supplemental material\n# Acknowledgments and contributions\n## Sources and related publications\n### In preparation work\n### Previously published work","[{\"question\":\"What problem does this dissertation address?\",\"answer\":\"It addresses how to infer cellular network structure and interaction patterns in systems biology using generalizable machine learning methods.\"},{\"question\":\"What is deciphR?\",\"answer\":\"deciphR is a dimensionality reduction framework and software package developed to infer networks of cellular coordination from single-cell genomics data.\"},{\"question\":\"How is the research related to prior publications?\",\"answer\":\"Chapters 3 and 4 include work previously published or in press in peer-reviewed journals, including single-cell mapping of hormoneregulated cell-cell interaction networks.\"}]","Generalizable machine learning methods for network inference in systems biology | 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