{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/79411"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/79411","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Identification of Supervised and Sparse Functional Genomic Pathways","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Zhang, Fan"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Miecznikowski, Jeffrey","Biostatistics"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-04-04T20:32:36Z","date_published":"2019-04-04T20:32:36Z","updated_at":"2026-07-27T19:05:16Z","subjects":["biostatistics"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/79411","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Miecznikowski, Jeffrey","Biostatistics"]},{"key":"dc:creator","label":"Author","values":["Zhang, Fan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-04-04T20:32:36Z","2019","2019-01-17 13:13:40"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["biostatistics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/79411"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Functional pathways involve a series of biological alterations that may result in the occurrence of many diseases including cancer. With the availability of various \"omics\" technologies it becomes feasible to integrate information from a hierarchy of biological layers to provide a more comprehensive understanding to the disease. In many diseases, it is believed that only a small number of networks, each relatively small in size , drive the disease. The goal in this dissertation is to develop methods to discover these func­tional networks across biological layers correlated with the phenotype. We derive a novel Network Summary Matrix (NSM) that highlights potential pathways conforming to least squares regression relationships. An algorithm called Decomposition of Network Summary Matrix via Instability (DNSMI) involving decomposition of NSM using instability regularization is proposed. Simulations and real data analysis from The Cancer Genome Atlas (TCGA) program will be shown to demonstrate the performance of the algorithm. In addition, this dissertation evaluated the significance of the selected pathway by DNSMI for different forms of summary statistics using permutation strategy as well as assessed the effect of instability threshold c5 on the significance of the algorithm result."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Identification of Supervised and Sparse Functional Genomic Pathways"]}]}],"canonical_facts":{"dc:contributor":["Miecznikowski, Jeffrey","Biostatistics"],"dc:creator":["Zhang, Fan"],"dc:date":["2019-04-04T20:32:36Z","2019","2019-01-17 13:13:40"],"dc:description":["Ph.D.","Functional pathways involve a series of biological alterations that may result in the occurrence of many diseases including cancer. With the availability of various \"omics\" technologies it becomes feasible to integrate information from a hierarchy of biological layers to provide a more comprehensive understanding to the disease. In many diseases, it is believed that only a small number of networks, each relatively small in size , drive the disease. The goal in this dissertation is to develop methods to discover these func­tional networks across biological layers correlated with the phenotype. We derive a novel Network Summary Matrix (NSM) that highlights potential pathways conforming to least squares regression relationships. An algorithm called Decomposition of Network Summary Matrix via Instability (DNSMI) involving decomposition of NSM using instability regularization is proposed. Simulations and real data analysis from The Cancer Genome Atlas (TCGA) program will be shown to demonstrate the performance of the algorithm. In addition, this dissertation evaluated the significance of the selected pathway by DNSMI for different forms of summary statistics using permutation strategy as well as assessed the effect of instability threshold c5 on the significance of the algorithm result."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/79411"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["biostatistics"],"dc:title":["Identification of Supervised and Sparse Functional Genomic Pathways"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:16Z"}