{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113873"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113873","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning for large and small data biomedical discovery","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_has_math":false,"creators":["Luo, Yunan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Peng, Jian","El-Kebir, Mohammed","Han, Jiawei","Ma, Jianzhu","Cho, Hyunghoon"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-29T21:34:36Z","date_published":"2022-04-29T21:34:36Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Computer science"],"languages":["en","eng"],"rights":["Copyright 2021 Yunan Luo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113873","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Peng, Jian","El-Kebir, Mohammed","Han, Jiawei","Ma, Jianzhu","Cho, Hyunghoon"]},{"key":"dc:creator","label":"Author","values":["Luo, Yunan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:34:36Z","2021-12","2021-11-30"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Yunan Luo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113873"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Yunan Luo, accepted the attached license on 2021-11-29 at 15:52.","The student, Yunan Luo, submitted this Dissertation for approval on 2021-11-29 at 16:04.","This Dissertation was approved for publication on 2021-11-30 at 13:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17293 on 2022-04-06 at 17:10:18","Made available in DSpace on 2022-04-29T21:34:36Z (GMT). No. of bitstreams: 3 LUO-DISSERTATION-2021.pdf: 7457567 bytes, checksum: 4e10f353b9cda3c7e3c14e1592be5e93 (MD5) LICENSE.txt: 4206 bytes, checksum: 372817f36626f0e2a23095efb1a8508e (MD5) PROQUEST_LICENSE.txt: 4552 bytes, checksum: e60970aaeda5aece606d55215b2146a8 (MD5) Previous issue date: 2021-11-30","In modern biomedicine, the role of computation becomes more crucial in light of the ever-increasing growth of biological data, which requires effective computational methods to integrate them in a meaningful way and unveil previously undiscovered biological insights. In this dissertation, we introduce a series of machine learning algorithms for biomedical discovery. Focused on protein functions in the context of system biology, these machine learning algorithms learn representations of protein sequences, structures, and networks in both the small- and large-data scenarios. First, we present a deep learning model that learns evolutionary contexts integrated representations of protein sequence and assists to discover protein variants with enhanced functions in protein engineering. Second, we describe a geometric deep learning model that learns representations of protein and compound structures to inform the prediction of protein-compound binding affinity. Third, we introduce a machine learning algorithm to integrate heterogeneous networks by learning compact network representations and to achieve drug repurposing by predicting novel drug-target interaction. We also present new scientific discoveries enabled by these machine learning algorithms. Taken together, this dissertation demonstrates the potential of machine learning to address the small- and large-data challenges of biomedical data and transform data into actionable insights and new discoveries."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine learning for large and small data biomedical discovery"]}]}],"canonical_facts":{"dc:contributor":["Peng, Jian","El-Kebir, Mohammed","Han, Jiawei","Ma, Jianzhu","Cho, Hyunghoon"],"dc:creator":["Luo, Yunan"],"dc:date":["2022-04-29T21:34:36Z","2021-12","2021-11-30"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Yunan Luo, accepted the attached license on 2021-11-29 at 15:52.","The student, Yunan Luo, submitted this Dissertation for approval on 2021-11-29 at 16:04.","This Dissertation was approved for publication on 2021-11-30 at 13:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17293 on 2022-04-06 at 17:10:18","Made available in DSpace on 2022-04-29T21:34:36Z (GMT). No. of bitstreams: 3 LUO-DISSERTATION-2021.pdf: 7457567 bytes, checksum: 4e10f353b9cda3c7e3c14e1592be5e93 (MD5) LICENSE.txt: 4206 bytes, checksum: 372817f36626f0e2a23095efb1a8508e (MD5) PROQUEST_LICENSE.txt: 4552 bytes, checksum: e60970aaeda5aece606d55215b2146a8 (MD5) Previous issue date: 2021-11-30","In modern biomedicine, the role of computation becomes more crucial in light of the ever-increasing growth of biological data, which requires effective computational methods to integrate them in a meaningful way and unveil previously undiscovered biological insights. In this dissertation, we introduce a series of machine learning algorithms for biomedical discovery. Focused on protein functions in the context of system biology, these machine learning algorithms learn representations of protein sequences, structures, and networks in both the small- and large-data scenarios. First, we present a deep learning model that learns evolutionary contexts integrated representations of protein sequence and assists to discover protein variants with enhanced functions in protein engineering. Second, we describe a geometric deep learning model that learns representations of protein and compound structures to inform the prediction of protein-compound binding affinity. Third, we introduce a machine learning algorithm to integrate heterogeneous networks by learning compact network representations and to achieve drug repurposing by predicting novel drug-target interaction. We also present new scientific discoveries enabled by these machine learning algorithms. Taken together, this dissertation demonstrates the potential of machine learning to address the small- and large-data challenges of biomedical data and transform data into actionable insights and new discoveries."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113873"],"dc:language":["en","eng"],"dc:rights":["Copyright 2021 Yunan Luo"],"dc:subject":["Computer science"],"dc:title":["Machine learning for large and small data biomedical discovery"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:53Z"}