<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>11(4)</volume><submitter>Sawai H</submitter><pubmed_abstract>&lt;h4>Unlabelled&lt;/h4>Bipolar disorder (BD) is a psychiatric disease considered to polygenic with multiple factors in genetics, each of which is not dominant but collaborative during pathogenic progression. We describe a method that estimates the collaborative contribution to the disease between a certain well-studied pathway and the other candidate pathway using Gene Set Enrichment Analysis (GSEA). We describe a modified GSEA (improved derivation) to identify genes that are significantly and differentially expressed between disease and non-disease states and that are consistently co-expressed with a target pathway which is deeply related to disease etiology. The modified GSEA uses available gene expression data to identify molecular mechanism (ubiquitin-proteasome and inflammatory response) </pubmed_abstract><journal>Bioinformation</journal><pagination>207-16</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC4479050</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Identification of collaborative activities with oxidative phosphorylation in bipolar disorder.</pubmed_title><pmcid>PMC4479050</pmcid><pubmed_authors>Tanaka H</pubmed_authors><pubmed_authors>Takai-Igarashi T</pubmed_authors><pubmed_authors>Sawai H</pubmed_authors></additional><is_claimable>false</is_claimable><name>Identification of collaborative activities with oxidative phosphorylation in bipolar disorder.</name><description>&lt;h4>Unlabelled&lt;/h4>Bipolar disorder (BD) is a psychiatric disease considered to polygenic with multiple factors in genetics, each of which is not dominant but collaborative during pathogenic progression. We describe a method that estimates the collaborative contribution to the disease between a certain well-studied pathway and the other candidate pathway using Gene Set Enrichment Analysis (GSEA). We describe a modified GSEA (improved derivation) to identify genes that are significantly and differentially expressed between disease and non-disease states and that are consistently co-expressed with a target pathway which is deeply related to disease etiology. The modified GSEA uses available gene expression data to identify molecular mechanism (ubiquitin-proteasome and inflammatory response) </description><dates><release>2015-01-01T00:00:00Z</release><publication>2015</publication><modification>2026-05-30T11:20:40.412Z</modification><creation>2019-03-27T01:53:59Z</creation></dates><accession>S-EPMC4479050</accession><cross_references><pubmed>26124562</pubmed><doi>10.6026/97320630011207</doi></cross_references></HashMap>