<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>12</volume><submitter>Zhou J</submitter><pubmed_abstract>&lt;h4>Background&lt;/h4>Ulcerative colitis (UC) is a complex inflammatory bowel disease with unclear etiology and challenging molecular mechanisms. This study aims to identify potential diagnostic and therapeutic biomarkers for UC through multi-omics integrative analysis, providing new insights into its precise diagnosis and treatment.&lt;h4>Methods&lt;/h4>Data samples from the Gene Expression Omnibus database and protein quantitative trait loci data from genome-wide association studies were integrated to identify overlapping genes. Three machine learning (ML) algorithms were employed to screen core hub genes from these overlapping genes, followed by the construction and external validation of a diagnostic model. Single-cell sequencing data were used to explore the expression profiles of core hub gen</pubmed_abstract><journal>Frontiers in molecular biosciences</journal><pagination>1686282</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12675271</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Multi-omics integration analysis based on plasma circulating proteins reveals potential therapeutic targets for ulcerative colitis.</pubmed_title><pmcid>PMC12675271</pmcid><pubmed_authors>Liu Z</pubmed_authors><pubmed_authors>Zhao W</pubmed_authors><pubmed_authors>Sun D</pubmed_authors><pubmed_authors>Lin Y</pubmed_authors><pubmed_authors>Yang B</pubmed_authors><pubmed_authors>Zhou J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Multi-omics integration analysis based on plasma circulating proteins reveals potential therapeutic targets for ulcerative colitis.</name><description>&lt;h4>Background&lt;/h4>Ulcerative colitis (UC) is a complex inflammatory bowel disease with unclear etiology and challenging molecular mechanisms. This study aims to identify potential diagnostic and therapeutic biomarkers for UC through multi-omics integrative analysis, providing new insights into its precise diagnosis and treatment.&lt;h4>Methods&lt;/h4>Data samples from the Gene Expression Omnibus database and protein quantitative trait loci data from genome-wide association studies were integrated to identify overlapping genes. Three machine learning (ML) algorithms were employed to screen core hub genes from these overlapping genes, followed by the construction and external validation of a diagnostic model. Single-cell sequencing data were used to explore the expression profiles of core hub gen</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025</publication><modification>2026-07-15T13:33:55.54Z</modification><creation>2026-07-05T03:08:30.459Z</creation></dates><accession>S-EPMC12675271</accession><cross_references><pubmed>41358199</pubmed><doi>10.3389/fmolb.2025.1686282</doi></cross_references></HashMap>