[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125485-en":3,"doc-seo-125485-105":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},125485,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Computational and Machine Learning Approaches for Functional Characterization of Chemical Compounds - Dissertation","Chemical-genetic interactions reveal cellular functions perturbed by compounds through genome-wide mutant fitness profiles. For each compound, the interaction profile summarizes how it affects every mutant in a collection, enabling systematic and unbiased functional inference. Large-scale profile libraries in Saccharomyces cerevisiae are used to improve functional prediction from chemical structures by benchmarking molecular fingerprints and similarity measures and by building a machine learning method for functional similarity. Multimodal interaction libraries further support scalable pipelines that integrate target scoring across modes or model target spread in genetic networks, with validation confirming newly identified targets.","Computational and machine learning approaches for functional characterization of chemical compounds using  \nchemical-genetic interactions  \nA DISSERTATION  \nSUBMITTED TO THE FACULTY OF THE  \nUNIVERSITY OF MINNESOTA  \nBY  \nHamid Safizadeh  \nIN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  \nDOCTOR OF PHILOSOPHY  \nAdvisor: Chad L. Myers, Ph.D .  \n© Hamid Safizadeh 2023  \nALL RIGHTS RESERVED  \nAcknowledgements  \nThe research work in this document is the product of a close collaboration among the researchers at the University of Minnesota, University of Toronto, and RIKEN Center for Sustainable Resource Science in Japan. These researchers are listed by their institution as follows:  \n• University of Minnesota: Chad Myers, Scott W. Simpkins, and Justin Nelson  \n• University of Toronto: Charles Boone, Sheena C. Li, Luis A. Vega Isuhuaylas, and Zhijian Li  \n• RIKEN Institute: Minoru Yoshida, Jeff S. Piotrowski, Hiroyuki Osada, Yoko Yashiroda, Hiroyuki Hirano, and Mami Yoshimura  \nThe details of their contributions are listed on the first pages of chapters 2–4.  \nDuring my doctoral program, I experienced difficulties including financial issues. I would like to acknowledge the office of International Student and Scholar Services (ISSS) that guided me through available financial resources for international students. Without the ISSS financial support, the completion of my doctoral program was surely impossible. In specific, I would like to thank Duane Rohovit and Drew Smith, the former ISSS advisors who always helped me navigate a solution to financial and academic problems.  \nMoreover, I would like to acknowledge and thank those who stayed with me during the dark time of my health crisis. First and foremost, I would like to thank Azadeh Etemadi, Christine Johns, Payman Najafi, and Shirin Asadi, who greatly supported me during this difficult time. I would also like to thank Alireza Nabavizadeh, Borzoo Baradaran, Paul and Adrienne Volk, Mohammad Amin Tadayon, Kiarash Yoosefi, Apollo Arman, Shahrbanou  \nAmirkeivan, Naty Lopez, Michael Jacobson, Spencer and Christine Chow, Cathy Moore, Wen Wang, Mahfuz Rahman, Farhad Shahriari Nogorani, Timothy McDonald, Scott and Kassandra Shimotsu, Mohsen Mahmoodi, Navid Noori, Julianne Pinke Vanderaa, Robert and Susan Osburn, Farhad Kosari, Alireza Foroozan, Morteza Salehi, and many others who provided much encouragement and comfort during the time of my health crisis. Without their support, the completion of this doctoral dissertation would have been very difficult, if not impossible. In addition, I would like to thank my siblings who always supported me during my doctoral program.  \nFinally, I would like to thank Professor Chad Myers, my advisor, who took the risk to accept me with limited background in biology and genetics as a doctoral student in his research group. Chad provided me with a unique opportunity to work in a supportive and collaborative research environment, where I received rigorous training in computational and systems biology. During my doctoral program, I encountered unexpected difficulties. I would like to sincerely thank Chad for his patience and support as I navigated solutions to those challenges. I would also like to thank Professor Mostafa (Mos) Kaveh, Professor Daniel Boley, and Professor Marc Riedel, who agreed to serve on my doctoral examination committee.  \nDedication  \nTo the loving memory of my parents, especially my amazing mother.  \nTo the memory of Manouchehr Harandi, who greatly assisted my academic pursuits. To Azadeh Etemadi for her endless support throughout the most difficult times of my life  \nin the United States.  \nAbstract  \nChemical-genetic interactions provide an invaluable source of information about the cellular functions perturbed by compounds. The chemical-genetic interaction profile of a compound against genome-wide mutant collections captures the effects of that compound on the fitness of each mutant in the collection. Since such profiles ","cbCairHsWByb2mEX","https://ap.wps.com/l/cbCairHsWByb2mEX","pdf",2765072,1,171,"English","en",105,"# Acknowledgements\n# Dedication\n# Abstract\n# List of Tables","[{\"question\":\"How do chemical-genetic interaction profiles help characterize compound functions?\",\"answer\":\"They capture genome-wide effects of a compound by measuring fitness changes across mutant collections, providing a systematic functional proxy for how compounds perturb cells.\"},{\"question\":\"What was done to improve functional prediction from chemical structure?\",\"answer\":\"The work benchmarked molecular fingerprints and similarity coefficients to identify combinations with superior prediction power, and introduced a machine learning approach to predict compound functional similarities from fingerprints.\"},{\"question\":\"How were compound targets identified using multimodal profiles?\",\"answer\":\"Two scalable pipelines were developed: one integrates chemical-genetic interactions across modes to produce unified compound–gene target scores, and another models target selection as inhibitory signal spreading across a genetic network, with validation supporting novel targets.\"}]","Computational and Machine Learning Approaches for Functional Characterization of Chemical Compounds - 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