[{"key":"dc.contributor.author","value":"Chen, Xiaohui","language":null},{"key":"dc.date.accessioned","value":"2009-02-02T17:28:13Z","language":null},{"key":"dc.date.available","value":"2009-02-02T17:28:13Z","language":null},{"key":"dc.date.issued","value":"2008","language":null},{"key":"dc.identifier.uri","value":"http:\/\/hdl.handle.net\/2429\/4068","language":null},{"key":"dc.description.abstract","value":"Genetic regulatory networks are of great importance in terms of scientific interests and practical medical importance. Since a number of high-throughput\nmeasurement devices are available, such as microarrays and\nsequencing techniques, regulatory networks have been intensively studied\nover the last decade. Based on these high-throughput data sets, statistical interpretations of these billions of bits are crucial for biologist to extract meaningful results. In this thesis, we compare a variety of existing\nregression models and apply them to construct regulatory networks which\nspan trancription factors and microRNAs. We also propose an extended\nalgorithm to address the local optimum issue in finding the Maximum A\nPosterjorj estimator. An E. coli mRNA expression microarray data set with\nknown bona fide interactions is used to evaluate our models and we show\nthat our regression networks with a properly chosen prior can perform comparably\nto the state-of-the-art regulatory network construction algorithm.\nFinally, we apply our models on a p53-related data set, NCI-60 data. By\nfurther incorporating available prior structural information from sequencing\ndata, we identify several significantly enriched interactions with cell proliferation\nfunction. In both of the two data sets, we select specific examples\nto show that many regulatory interactions can be confirmed by previous\nstudies or functional enrichment analysis. Through comparing statistical\nmodels, we conclude from the project that combining different models with\nover-representation analysis and prior structural information can improve\nthe quality of prediction and facilitate biological interpretation.\nKeywords: regulatory network, variable selection, penalized maximum\nlikelihood estimation, optimization, functional enrichment analysis.","language":"en"},{"key":"dc.format.extent","value":"2223158 bytes","language":null},{"key":"dc.format.mimetype","value":"application\/pdf","language":null},{"key":"dc.language.iso","value":"eng","language":"en"},{"key":"dc.publisher","value":"University of British Columbia","language":null},{"key":"dc.rights","value":"Attribution-NonCommercial-NoDerivatives 4.0 International","language":null},{"key":"dc.rights.uri","value":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/","language":null},{"key":"dc.title","value":"Comparisons of statistical modeling for constructing gene regulatory networks","language":"en"},{"key":"dc.type","value":"Text","language":null},{"key":"dc.degree.name","value":"Master of Science - MSc","language":"en"},{"key":"dc.degree.discipline","value":"Bioinformatics","language":"en"},{"key":"dc.degree.grantor","value":"University of British Columbia","language":null},{"key":"dc.date.graduation","value":"2008-11","language":"en"},{"key":"dc.type.text","value":"Thesis\/Dissertation","language":"en"},{"key":"dc.description.affiliation","value":"Science, Faculty of","language":null},{"key":"dc.degree.campus","value":"UBCV","language":"en"},{"key":"dc.description.scholarlevel","value":"Graduate","language":"en"}]