{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90839"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90839","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards the integration of genomic profiles and gene interaction networks for machine learning","abstract":"\"With the advent of big data, scientists are collecting biological data faster than they have in the past, including genomic profiles which describe individuals by thousands of genes at a time. Adding to this library of knowledge are gene interaction networks, which model overarching cellular processes by describing how genes interact with each other. When approached with genomic profile data together with gene interaction data, it becomes a question of how to integrate these two pieces of knowledge together for machine learning. Previous studies have attempted to employ some form of feature engineering process to \"\"collapse\"\" the network topology alongside the genomic profiles, losing the potential for global network information. Instead, we explore a framework based upon network propagation. We explain how network propagation algorithms can enhance standalone genomic profiles, called embeddings, and show these enhancements lead to improved predictive accuracies on drug response classification. We next show that these embeddings contain predictive signals that are not necessarily implicated by gene ranking methods such as PageRank. Last, we apply network propagation to a dataset presented by the DREAM organization, and show we can improve a naive linear regression that solves for a drug sensitive ranking task.\"","abstract_html":""With the advent of big data, scientists are collecting biological data faster than they have in the past, including genomic profiles which describe individuals by thousands of genes at a time. Adding to this library of knowledge are gene interaction networks, which model overarching cellular processes by describing how genes interact with each other. When approached with genomic profile data together with gene interaction data, it becomes a question of how to integrate these two pieces of knowledge together for machine learning. Previous studies have attempted to employ some form of feature engineering process to ""collapse"" the network topology alongside the genomic profiles, losing the potential for global network information. Instead, we explore a framework based upon network propagation. We explain how network propagation algorithms can enhance standalone genomic profiles, called embeddings, and show these enhancements lead to improved predictive accuracies on drug response classification. We next show that these embeddings contain predictive signals that are not necessarily implicated by gene ranking methods such as PageRank. Last, we apply network propagation to a dataset presented by the DREAM organization, and show we can improve a naive linear regression that solves for a drug sensitive ranking task."","abstract_has_math":false,"creators":["Lin, Henry A"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T20:35:21Z","date_published":"2016-07-07T20:35:21Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Drug response","Random Walk with Restart","Network Propagation","PageRank","Embedding"],"languages":["en"],"rights":["Copyright May 2016 by Henry A. Lin. All rights reserved."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90839","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Lin, Henry A"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T20:35:21Z","2018-07-08T09:15:27Z","2016-04-27","2016-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["Drug response","Random Walk with Restart","Network Propagation","PageRank","Embedding"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright May 2016 by Henry A. Lin. All rights reserved."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90839"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"With the advent of big data, scientists are collecting biological data faster than they have in the past, including genomic profiles which describe individuals by thousands of genes at a time. Adding to this library of knowledge are gene interaction networks, which model overarching cellular processes by describing how genes interact with each other. When approached with genomic profile data together with gene interaction data, it becomes a question of how to integrate these two pieces of knowledge together for machine learning. Previous studies have attempted to employ some form of feature engineering process to \"\"collapse\"\" the network topology alongside the genomic profiles, losing the potential for global network information. Instead, we explore a framework based upon network propagation. We explain how network propagation algorithms can enhance standalone genomic profiles, called embeddings, and show these enhancements lead to improved predictive accuracies on drug response classification. We next show that these embeddings contain predictive signals that are not necessarily implicated by gene ranking methods such as PageRank. Last, we apply network propagation to a dataset presented by the DREAM organization, and show we can improve a naive linear regression that solves for a drug sensitive ranking task.\"","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Henry Lin, accepted the attached license on 2016-04-26 at 15:12.","The student, Henry Lin, submitted this Thesis for approval on 2016-04-26 at 15:30.","This Thesis was approved for publication on 2016-04-27 at 17:16.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9521 on 2016-07-07 at 13:50:59","Made available in DSpace on 2016-07-07T20:35:21Z (GMT). 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Adding to this library of knowledge are gene interaction networks, which model overarching cellular processes by describing how genes interact with each other. When approached with genomic profile data together with gene interaction data, it becomes a question of how to integrate these two pieces of knowledge together for machine learning. Previous studies have attempted to employ some form of feature engineering process to \"\"collapse\"\" the network topology alongside the genomic profiles, losing the potential for global network information. Instead, we explore a framework based upon network propagation. We explain how network propagation algorithms can enhance standalone genomic profiles, called embeddings, and show these enhancements lead to improved predictive accuracies on drug response classification. We next show that these embeddings contain predictive signals that are not necessarily implicated by gene ranking methods such as PageRank. Last, we apply network propagation to a dataset presented by the DREAM organization, and show we can improve a naive linear regression that solves for a drug sensitive ranking task.\"","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Henry Lin, accepted the attached license on 2016-04-26 at 15:12.","The student, Henry Lin, submitted this Thesis for approval on 2016-04-26 at 15:30.","This Thesis was approved for publication on 2016-04-27 at 17:16.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9521 on 2016-07-07 at 13:50:59","Made available in DSpace on 2016-07-07T20:35:21Z (GMT). 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All rights reserved."],"dc:subject":["Drug response","Random Walk with Restart","Network Propagation","PageRank","Embedding"],"dc:title":["Towards the integration of genomic profiles and gene interaction networks for machine learning"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:34Z"}