{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101057"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101057","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"GeneSet MAPR: Characterization of gene sets through heterogeneous network patterns","abstract":"Often, machine learning and big data concepts are applied to problems without a proper appreciation of their limitations or domain context. At the same time there is a growing appreciation for the ability of networks to represent more complex connections between data points than previous structures. However, established machine learning approaches rarely take advantage of such structures and must be adapted. We present here a method that utilizes patterns of connections within heterogeneous networks to score items by their similarity to an input set. We apply the idea of meta-paths as an abstraction to counteract typical big data problems of noise and overfitting. We also aim to demystify the black-box nature of machine learning by providing intuitive feedback about why items are considered similar. While the method presented here is generalizable to any domain, the specific examples explored are within the genomics domain. The final tool, GeneSet MAPR, is especially useful in a domain with little ground truth and a huge volume of noisy, uncertain data. We show that GeneSet MAPR performs better at discovering related but concealed data points than an approach using the same data without abstraction, as well as a an established state-of-the-art approach that works on a network but ignores the heterogeneous patterns. It does this while providing details the other methods cannot.","abstract_html":"Often, machine learning and big data concepts are applied to problems without a proper appreciation of their limitations or domain context. At the same time there is a growing appreciation for the ability of networks to represent more complex connections between data points than previous structures. However, established machine learning approaches rarely take advantage of such structures and must be adapted. We present here a method that utilizes patterns of connections within heterogeneous networks to score items by their similarity to an input set. We apply the idea of meta-paths as an abstraction to counteract typical big data problems of noise and overfitting. We also aim to demystify the black-box nature of machine learning by providing intuitive feedback about why items are considered similar. While the method presented here is generalizable to any domain, the specific examples explored are within the genomics domain. The final tool, GeneSet MAPR, is especially useful in a domain with little ground truth and a huge volume of noisy, uncertain data. We show that GeneSet MAPR performs better at discovering related but concealed data points than an approach using the same data without abstraction, as well as a an established state-of-the-art approach that works on a network but ignores the heterogeneous patterns. It does this while providing details the other methods cannot.","abstract_has_math":false,"creators":["Linkowski, Gregory"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Vasudevan, Shobha"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:27:26Z","date_published":"2018-09-04T20:27:26Z","updated_at":"2026-07-22T22:24:38Z","subjects":["graph theory","network","meta-paths","bioinformatics","machine learning","pattern recognition","big data","statistical analysis","p-value"],"languages":["en"],"rights":["Copyright 2018 Gregory Linkowski"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101057","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Vasudevan, Shobha"]},{"key":"dc:creator","label":"Author","values":["Linkowski, Gregory"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:27:26Z","2018-04-24","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["graph theory","network","meta-paths","bioinformatics","machine learning","pattern recognition","big data","statistical analysis","p-value"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Gregory Linkowski"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101057"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Often, machine learning and big data concepts are applied to problems without a proper appreciation of their limitations or domain context. At the same time there is a growing appreciation for the ability of networks to represent more complex connections between data points than previous structures. However, established machine learning approaches rarely take advantage of such structures and must be adapted. We present here a method that utilizes patterns of connections within heterogeneous networks to score items by their similarity to an input set. We apply the idea of meta-paths as an abstraction to counteract typical big data problems of noise and overfitting. We also aim to demystify the black-box nature of machine learning by providing intuitive feedback about why items are considered similar. While the method presented here is generalizable to any domain, the specific examples explored are within the genomics domain. The final tool, GeneSet MAPR, is especially useful in a domain with little ground truth and a huge volume of noisy, uncertain data. We show that GeneSet MAPR performs better at discovering related but concealed data points than an approach using the same data without abstraction, as well as a an established state-of-the-art approach that works on a network but ignores the heterogeneous patterns. It does this while providing details the other methods cannot.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Gregory Linkowski, accepted the attached license on 2018-04-24 at 15:05.","The student, Gregory Linkowski, submitted this Thesis for approval on 2018-04-24 at 15:20.","This Thesis was approved for publication on 2018-04-24 at 15:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12452 on 2018-08-31 at 17:14:32","Made available in DSpace on 2018-09-04T20:27:26Z (GMT). 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However, established machine learning approaches rarely take advantage of such structures and must be adapted. We present here a method that utilizes patterns of connections within heterogeneous networks to score items by their similarity to an input set. We apply the idea of meta-paths as an abstraction to counteract typical big data problems of noise and overfitting. We also aim to demystify the black-box nature of machine learning by providing intuitive feedback about why items are considered similar. While the method presented here is generalizable to any domain, the specific examples explored are within the genomics domain. The final tool, GeneSet MAPR, is especially useful in a domain with little ground truth and a huge volume of noisy, uncertain data. 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