{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/24023"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/24023","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Meta-analysis of liver transcriptomic data identifies accurate disease classifiers and disease perturbed networks","abstract":"Chronic liver diseases are a major health problem. Previous DNA microarray studies of different liver diseases have improved our knowledge of the molecular pathogenesis of liver diseases and produced potential biomarkers. However, these studies typically rely on binary phenotype comparisons (e.g. cancer vs. normal) to identify disease signatures. It is possible that the resulting signatures may be partially shared by other liver diseases not included in the binary comparison. In this study, we took a comprehensive and organ-specific approach, where we studied all liver pathophysiological states in a single unifying context, and found a specific transcriptomic signature for each phenotype with respect to all the other phenotypes, instead of just one. The resulting 36-gene disease signature had 85% accuracy in 10 fold cross validation. Through stringent leave-one-lab out independent validation, we found that high classification accuracy was achieved when there was a total of around 100 samples from 2 independent contributing labs. We also identified perturbed networks in liver diseases in general and hepatocellular carcinoma in particular. Many of the classifier genes and perturbed networks are involved in important biological processes in liver disease pathogenesis, including immune response and inflammation, fibrogenesis, metabolism and its regulation, apoptosis, and cellular signaling. The disease classifiers and perturbed networks identified in this study may be potential candidates for novel diagnostic approaches to multiple liver diseases.","abstract_html":"Chronic liver diseases are a major health problem. Previous DNA microarray studies of different liver diseases have improved our knowledge of the molecular pathogenesis of liver diseases and produced potential biomarkers. However, these studies typically rely on binary phenotype comparisons (e.g. cancer vs. normal) to identify disease signatures. It is possible that the resulting signatures may be partially shared by other liver diseases not included in the binary comparison. In this study, we took a comprehensive and organ-specific approach, where we studied all liver pathophysiological states in a single unifying context, and found a specific transcriptomic signature for each phenotype with respect to all the other phenotypes, instead of just one. The resulting 36-gene disease signature had 85% accuracy in 10 fold cross validation. Through stringent leave-one-lab out independent validation, we found that high classification accuracy was achieved when there was a total of around 100 samples from 2 independent contributing labs. We also identified perturbed networks in liver diseases in general and hepatocellular carcinoma in particular. Many of the classifier genes and perturbed networks are involved in important biological processes in liver disease pathogenesis, including immune response and inflammation, fibrogenesis, metabolism and its regulation, apoptosis, and cellular signaling. The disease classifiers and perturbed networks identified in this study may be potential candidates for novel diagnostic approaches to multiple liver diseases.","abstract_has_math":false,"creators":["Wang, Yuliang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Chemical Engineering","degree_department":null,"school":null,"contributors":["Price, Nathan D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-25T15:06:24Z","date_published":"2011-05-25T15:06:24Z","updated_at":"2026-07-22T22:25:24Z","subjects":["microarray","meta-analysis","disease transcriptomic signature","network perturbation"],"languages":["en"],"rights":["Copywright 2011 Yuliang Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/24023","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Price, Nathan D."]},{"key":"dc:creator","label":"Author","values":["Wang, Yuliang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-25T15:06:24Z","2011-05"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Chemical Engineering"]},{"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":["microarray","meta-analysis","disease transcriptomic signature","network perturbation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copywright 2011 Yuliang Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/24023"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Chronic liver diseases are a major health problem. Previous DNA microarray studies of different liver diseases have improved our knowledge of the molecular pathogenesis of liver diseases and produced potential biomarkers. However, these studies typically rely on binary phenotype comparisons (e.g. cancer vs. normal) to identify disease signatures. It is possible that the resulting signatures may be partially shared by other liver diseases not included in the binary comparison. In this study, we took a comprehensive and organ-specific approach, where we studied all liver pathophysiological states in a single unifying context, and found a specific transcriptomic signature for each phenotype with respect to all the other phenotypes, instead of just one. The resulting 36-gene disease signature had 85% accuracy in 10 fold cross validation. Through stringent leave-one-lab out independent validation, we found that high classification accuracy was achieved when there was a total of around 100 samples from 2 independent contributing labs. We also identified perturbed networks in liver diseases in general and hepatocellular carcinoma in particular. Many of the classifier genes and perturbed networks are involved in important biological processes in liver disease pathogenesis, including immune response and inflammation, fibrogenesis, metabolism and its regulation, apoptosis, and cellular signaling. The disease classifiers and perturbed networks identified in this study may be potential candidates for novel diagnostic approaches to multiple liver diseases.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-04-25T13:32:30Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Wang_Yuliang.docx: 1631389 bytes, checksum: 531e69648a10554db60409d00b18e3d6 (MD5) Wang_Yuliang.pdf: 2191693 bytes, checksum: 9ca2b6e77aa65ea0bf9b1a21dfc0f94d (MD5)","Made available in DSpace on 2011-05-25T15:06:24Z (GMT). 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However, these studies typically rely on binary phenotype comparisons (e.g. cancer vs. normal) to identify disease signatures. It is possible that the resulting signatures may be partially shared by other liver diseases not included in the binary comparison. In this study, we took a comprehensive and organ-specific approach, where we studied all liver pathophysiological states in a single unifying context, and found a specific transcriptomic signature for each phenotype with respect to all the other phenotypes, instead of just one. The resulting 36-gene disease signature had 85% accuracy in 10 fold cross validation. Through stringent leave-one-lab out independent validation, we found that high classification accuracy was achieved when there was a total of around 100 samples from 2 independent contributing labs. We also identified perturbed networks in liver diseases in general and hepatocellular carcinoma in particular. 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