{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/77535"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/77535","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Multinet Bayesian network models for large-scale transcriptome integration in computational medicine","abstract":"Motivation: This work utilizes the closed loop Bayesian network framework for predictive medicine via integrative analysis of publicly available gene expression findings pertaining to various diseases and analyzes the results to determine which model, single net or multinet, is a more accurate predictor for determining disease status. Results: In general, it is suggested to use the multinet Bayesian network framework for predictive medicine instead of the single net Bayesian network, because for large numbers of samples and features, it is highly likely that it is the stronger predictor, and for smaller numbers of samples and features, if the multinet returns good results, it is likely to be a better predictor than the single net Bayesian network.","abstract_html":"Motivation: This work utilizes the closed loop Bayesian network framework for predictive medicine via integrative analysis of publicly available gene expression findings pertaining to various diseases and analyzes the results to determine which model, single net or multinet, is a more accurate predictor for determining disease status. Results: In general, it is suggested to use the multinet Bayesian network framework for predictive medicine instead of the single net Bayesian network, because for large numbers of samples and features, it is highly likely that it is the stronger predictor, and for smaller numbers of samples and features, if the multinet returns good results, it is likely to be a better predictor than the single net Bayesian network.","abstract_has_math":false,"creators":["Lin, Tiffany J"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Gil Alterovitz."],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-22T22:21:27Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/77535","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Gil Alterovitz."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.","Cataloged from PDF version of thesis.","Includes bibliographical references (p. 30)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Motivation: This work utilizes the closed loop Bayesian network framework for predictive medicine via integrative analysis of publicly available gene expression findings pertaining to various diseases and analyzes the results to determine which model, single net or multinet, is a more accurate predictor for determining disease status. Results: In general, it is suggested to use the multinet Bayesian network framework for predictive medicine instead of the single net Bayesian network, because for large numbers of samples and features, it is highly likely that it is the stronger predictor, and for smaller numbers of samples and features, if the multinet returns good results, it is likely to be a better predictor than the single net Bayesian network."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Multinet Bayesian network models for large-scale transcriptome integration in computational medicine"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gil Alterovitz."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. 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Results: In general, it is suggested to use the multinet Bayesian network framework for predictive medicine instead of the single net Bayesian network, because for large numbers of samples and features, it is highly likely that it is the stronger predictor, and for smaller numbers of samples and features, if the multinet returns good results, it is likely to be a better predictor than the single net Bayesian network."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/77535"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Multinet Bayesian network models for large-scale transcriptome integration in computational medicine"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:27Z"}