{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/44754"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/44754","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"NCIS: a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression","abstract":"Cancer subtype information is critically important for designing more effective treatments. In this thesis, we introduce a new co-clustering algorithm for cancer subtype identification, which combines the information of gene networks to simultaneously group samples and genes into biologically meaningful clusters. We call our method network-assisted co-clustering for the identification of cancer subtypes (NCIS). Prior to clustering, we assign weights to genes: those that play key roles in the network and/or show significant variations among samples would be prioritized. This new approach allows us to rely more on genes that are informative and representative by including the weights as an importance indicator in the clustering step. Here we introduce a new weighted co-clustering method based on semi-nonnegative matrix tri-factorization. We evaluated the effectiveness of the algorithm on large-scale Glioblastoma multiforme (GBM) and breast cancer (BRCA) datasets from TCGA and on simulated datasets. We found that our NCIS method can achieve more reliable results with respect to the clinical features compared to conventional semi-nonnegative matrix tri-factorization methods and consensus clustering. We also train two classifiers for GBM and BRCA subtypes identification based on NCIS's results. This new method will be very useful to comprehensively detect subtypes that are otherwise obscured by cancer heterogeneity, from various types of cancers based on high-throughput and high-dimensional gene expression data.","abstract_html":"Cancer subtype information is critically important for designing more effective treatments. In this thesis, we introduce a new co-clustering algorithm for cancer subtype identification, which combines the information of gene networks to simultaneously group samples and genes into biologically meaningful clusters. We call our method network-assisted co-clustering for the identification of cancer subtypes (NCIS). Prior to clustering, we assign weights to genes: those that play key roles in the network and/or show significant variations among samples would be prioritized. This new approach allows us to rely more on genes that are informative and representative by including the weights as an importance indicator in the clustering step. Here we introduce a new weighted co-clustering method based on semi-nonnegative matrix tri-factorization. We evaluated the effectiveness of the algorithm on large-scale Glioblastoma multiforme (GBM) and breast cancer (BRCA) datasets from TCGA and on simulated datasets. We found that our NCIS method can achieve more reliable results with respect to the clinical features compared to conventional semi-nonnegative matrix tri-factorization methods and consensus clustering. We also train two classifiers for GBM and BRCA subtypes identification based on NCIS's results. This new method will be very useful to comprehensively detect subtypes that are otherwise obscured by cancer heterogeneity, from various types of cancers based on high-throughput and high-dimensional gene expression data.","abstract_has_math":false,"creators":["Liu, Yiyi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Bioengineering","degree_department":null,"school":null,"contributors":["Ma, Jian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-28T19:18:24Z","date_published":"2013-05-28T19:18:24Z","updated_at":"2026-07-22T22:25:34Z","subjects":["Cancer Subtype","Co-clustering","Gene Expression"],"languages":["en"],"rights":["Copyright 2013 Yiyi Liu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/44754","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ma, Jian"]},{"key":"dc:creator","label":"Author","values":["Liu, Yiyi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-05-28T19:18:24Z","2015-05-28T10:01:18Z","2013-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Bioengineering"]},{"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":["Cancer Subtype","Co-clustering","Gene Expression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Yiyi Liu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/44754"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Cancer subtype information is critically important for designing more effective treatments. In this thesis, we introduce a new co-clustering algorithm for cancer subtype identification, which combines the information of gene networks to simultaneously group samples and genes into biologically meaningful clusters. We call our method network-assisted co-clustering for the identification of cancer subtypes (NCIS). Prior to clustering, we assign weights to genes: those that play key roles in the network and/or show significant variations among samples would be prioritized. This new approach allows us to rely more on genes that are informative and representative by including the weights as an importance indicator in the clustering step. Here we introduce a new weighted co-clustering method based on semi-nonnegative matrix tri-factorization. We evaluated the effectiveness of the algorithm on large-scale Glioblastoma multiforme (GBM) and breast cancer (BRCA) datasets from TCGA and on simulated datasets. We found that our NCIS method can achieve more reliable results with respect to the clinical features compared to conventional semi-nonnegative matrix tri-factorization methods and consensus clustering. We also train two classifiers for GBM and BRCA subtypes identification based on NCIS's results. This new method will be very useful to comprehensively detect subtypes that are otherwise obscured by cancer heterogeneity, from various types of cancers based on high-throughput and high-dimensional gene expression data.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-04-23T18:50:56Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Liu_Yiyi.pdf: 1234634 bytes, checksum: d7e48e1cc4acddadb03d46b3c79cb770 (MD5)","Made available in DSpace on 2013-05-28T19:18:24Z (GMT). No. of bitstreams: 2 Yiyi_Liu.pdf: 1234634 bytes, checksum: d7e48e1cc4acddadb03d46b3c79cb770 (MD5) license.txt: 4058 bytes, checksum: e310e00931452a21051e9bce8674641f (MD5)","Item marked as restricted to the 'Administrator' Group (id=1) by Seth Robbins (srobbins@illinois.edu) on 2013-05-28T19:21:28Z Item is restricted until 2015-05-28T19:21:22Z","Restriction data tranferred 2014-07-01T11:16:33-05:00 Original Data Group with Access Administrator Release Date: 2015-05-28 14:21:22 UTC Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 44729 on 2015-05-28T10:01:18Z."]},{"key":"dc:title","label":"Title","values":["NCIS: a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression"]}]}],"canonical_facts":{"dc:contributor":["Ma, Jian"],"dc:creator":["Liu, Yiyi"],"dc:date":["2013-05-28T19:18:24Z","2015-05-28T10:01:18Z","2013-05"],"dc:description":["Cancer subtype information is critically important for designing more effective treatments. In this thesis, we introduce a new co-clustering algorithm for cancer subtype identification, which combines the information of gene networks to simultaneously group samples and genes into biologically meaningful clusters. We call our method network-assisted co-clustering for the identification of cancer subtypes (NCIS). Prior to clustering, we assign weights to genes: those that play key roles in the network and/or show significant variations among samples would be prioritized. This new approach allows us to rely more on genes that are informative and representative by including the weights as an importance indicator in the clustering step. Here we introduce a new weighted co-clustering method based on semi-nonnegative matrix tri-factorization. We evaluated the effectiveness of the algorithm on large-scale Glioblastoma multiforme (GBM) and breast cancer (BRCA) datasets from TCGA and on simulated datasets. We found that our NCIS method can achieve more reliable results with respect to the clinical features compared to conventional semi-nonnegative matrix tri-factorization methods and consensus clustering. We also train two classifiers for GBM and BRCA subtypes identification based on NCIS's results. This new method will be very useful to comprehensively detect subtypes that are otherwise obscured by cancer heterogeneity, from various types of cancers based on high-throughput and high-dimensional gene expression data.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-04-23T18:50:56Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Liu_Yiyi.pdf: 1234634 bytes, checksum: d7e48e1cc4acddadb03d46b3c79cb770 (MD5)","Made available in DSpace on 2013-05-28T19:18:24Z (GMT). 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