{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/78566"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/78566","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Methods for Functional Module Detection with Application to Biological Networks","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Yu, Han"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Blair, Rachael","Biostatistics"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-10-26T02:55:40Z","date_published":"2018-10-26T02:55:40Z","updated_at":"2026-07-27T19:05:12Z","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/10477/78566","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Blair, Rachael","Biostatistics"]},{"key":"dc:creator","label":"Author","values":["Yu, Han"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-10-26T02:55:40Z","2018","2018-08-07 17:13:10"]},{"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/10477/78566"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Networks provide a mathematical representation of variables and their dependencies. Hierarchical organization and network structure are naturally occurring in many areas, e.g., biology, ecology and social settings. Thus, network analysis holds tremendous promise for generating insights that underpin some of the driving complex questions in disease. This dissertation tackles some of the modern challenges in network analysis, including (1) module (community) detection, (2) stability analysis, and (3) probabilistic inferences and (4) the integration of metabolic and genetic networks. These contributions and select applications are briefly described. The focus is on biological networks, which have inherent associations and dependencies. Often, in addition to the network structure there is additional information available about the nodes, known as attribute data (e.g., demographics, expression levels, pathway memberships). Our hypothesis, which is rooted in the principle of homophily, is that functional modules in attributed networks are characterized by densely connected group with relatively homogeneous attributes. Two novel approaches that bridge the structural and attribute space for module detection are described. These approaches integrate mixed node attributes with module detection and have the capability of selecting only relevant attributes. Application to a human protein-protein interaction network and breast cancer data, revealed modules with central regulators, which cannot be found by using either network or attribute data alone. In the second part of this work, a sampling based approach is developed to evaluate the stability of general clustering results on cluster and individual levels. The stability measure, as a surrogate to the clustering variability, can be applied to the module detection to assess its quality. In the final parts of this work, the activity of functional modules and biological processes are linked through Bayesian networks. To achieve this, we developed the first open source R package to perform probabilistic reasoning in conditional Gaussian Bayesian networks. A Bayesian network of the human HIF-1 signaling pathway was built. We combined the Bayesian network with deterministic models of cellular metabolism to predict the systematic effect of modulating HIF-1 activity on the energy metabolism in brain cells of Alzheimer's Disease patients."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Methods for Functional Module Detection with Application to Biological Networks"]}]}],"canonical_facts":{"dc:contributor":["Blair, Rachael","Biostatistics"],"dc:creator":["Yu, Han"],"dc:date":["2018-10-26T02:55:40Z","2018","2018-08-07 17:13:10"],"dc:description":["Ph.D.","Networks provide a mathematical representation of variables and their dependencies. Hierarchical organization and network structure are naturally occurring in many areas, e.g., biology, ecology and social settings. Thus, network analysis holds tremendous promise for generating insights that underpin some of the driving complex questions in disease. This dissertation tackles some of the modern challenges in network analysis, including (1) module (community) detection, (2) stability analysis, and (3) probabilistic inferences and (4) the integration of metabolic and genetic networks. These contributions and select applications are briefly described. The focus is on biological networks, which have inherent associations and dependencies. Often, in addition to the network structure there is additional information available about the nodes, known as attribute data (e.g., demographics, expression levels, pathway memberships). Our hypothesis, which is rooted in the principle of homophily, is that functional modules in attributed networks are characterized by densely connected group with relatively homogeneous attributes. Two novel approaches that bridge the structural and attribute space for module detection are described. These approaches integrate mixed node attributes with module detection and have the capability of selecting only relevant attributes. Application to a human protein-protein interaction network and breast cancer data, revealed modules with central regulators, which cannot be found by using either network or attribute data alone. In the second part of this work, a sampling based approach is developed to evaluate the stability of general clustering results on cluster and individual levels. The stability measure, as a surrogate to the clustering variability, can be applied to the module detection to assess its quality. In the final parts of this work, the activity of functional modules and biological processes are linked through Bayesian networks. To achieve this, we developed the first open source R package to perform probabilistic reasoning in conditional Gaussian Bayesian networks. A Bayesian network of the human HIF-1 signaling pathway was built. We combined the Bayesian network with deterministic models of cellular metabolism to predict the systematic effect of modulating HIF-1 activity on the energy metabolism in brain cells of Alzheimer's Disease patients."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/78566"],"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":["Methods for Functional Module Detection with Application to Biological Networks"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:12Z"}