{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108040"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108040","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning for biological networks","abstract":"Genetic studies often involve huge number of covariants that interact with each other, in the form of expressions or mutations. It is crucial to mine important covariants associated with different diseases for better clinical treatment. Traditional statistical methods have been successful in testing single covariants, but are limited when studying the joint effect of multiple related genes. Hence, incorporating biological interaction networks becomes a promising approach for genetic association study. On the other hand, the advance of graph learning algorithms has made it possible to build data-driven models for large graph problems. These methods generally fall into two categories: 1) random walk and 2) deep graph neural net. We study how to leverage information from biological networks under these frameworks to solve genetic association problems on large scale. Towards this end, we have applied graph neural network to cancer prognostic prediction. We also develop a network diffusion method for variant association study for Parkinson's disease. Our results demonstrate the power of graph learning algorithms in biological domain.","abstract_html":"Genetic studies often involve huge number of covariants that interact with each other, in the form of expressions or mutations. It is crucial to mine important covariants associated with different diseases for better clinical treatment. Traditional statistical methods have been successful in testing single covariants, but are limited when studying the joint effect of multiple related genes. Hence, incorporating biological interaction networks becomes a promising approach for genetic association study. On the other hand, the advance of graph learning algorithms has made it possible to build data-driven models for large graph problems. These methods generally fall into two categories: 1) random walk and 2) deep graph neural net. We study how to leverage information from biological networks under these frameworks to solve genetic association problems on large scale. Towards this end, we have applied graph neural network to cancer prognostic prediction. We also develop a network diffusion method for variant association study for Parkinson's disease. Our results demonstrate the power of graph learning algorithms in biological domain.","abstract_has_math":false,"creators":["Ding, Hantian"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Peng, Jian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T21:58:03Z","date_published":"2020-08-26T21:58:03Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Machine Learning","Bioinformatics"],"languages":["en"],"rights":["Copyright 2020 Hantian Ding"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108040","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Peng, Jian"]},{"key":"dc:creator","label":"Author","values":["Ding, Hantian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T21:58:03Z","2020-05-14","2020-05"]},{"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":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Machine Learning","Bioinformatics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Hantian Ding"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108040"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Genetic studies often involve huge number of covariants that interact with each other, in the form of expressions or mutations. It is crucial to mine important covariants associated with different diseases for better clinical treatment. Traditional statistical methods have been successful in testing single covariants, but are limited when studying the joint effect of multiple related genes. Hence, incorporating biological interaction networks becomes a promising approach for genetic association study. On the other hand, the advance of graph learning algorithms has made it possible to build data-driven models for large graph problems. These methods generally fall into two categories: 1) random walk and 2) deep graph neural net. We study how to leverage information from biological networks under these frameworks to solve genetic association problems on large scale. Towards this end, we have applied graph neural network to cancer prognostic prediction. We also develop a network diffusion method for variant association study for Parkinson's disease. Our results demonstrate the power of graph learning algorithms in biological domain.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Hantian Ding, accepted the attached license on 2020-05-11 at 17:39.","The student, Hantian Ding, submitted this Thesis for approval on 2020-05-12 at 11:55.","This Thesis was approved for publication on 2020-05-14 at 08:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15339 on 2020-08-25 at 17:14:05","Made available in DSpace on 2020-08-26T21:58:03Z (GMT). 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Hence, incorporating biological interaction networks becomes a promising approach for genetic association study. On the other hand, the advance of graph learning algorithms has made it possible to build data-driven models for large graph problems. These methods generally fall into two categories: 1) random walk and 2) deep graph neural net. We study how to leverage information from biological networks under these frameworks to solve genetic association problems on large scale. Towards this end, we have applied graph neural network to cancer prognostic prediction. We also develop a network diffusion method for variant association study for Parkinson's disease. Our results demonstrate the power of graph learning algorithms in biological domain.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Hantian Ding, accepted the attached license on 2020-05-11 at 17:39.","The student, Hantian Ding, submitted this Thesis for approval on 2020-05-12 at 11:55.","This Thesis was approved for publication on 2020-05-14 at 08:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15339 on 2020-08-25 at 17:14:05","Made available in DSpace on 2020-08-26T21:58:03Z (GMT). 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