{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Huan T"],"funding":["NIDA NIH HHS","NIMH NIH HHS","Biological and Environmental Research","National Institute of Mental Health","National Institute of General Medical Sciences","National Institute on Drug Abuse","NIGMS NIH HHS"],"pagination":["8396-8403"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10273218"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["90(14)"],"pubmed_abstract":["Comprehensive metabolomic data can be achieved using multiple orthogonal separation and mass spectrometry (MS) analytical techniques. However, drawing biologically relevant conclusions from this data and combining it with additional layers of information collected by other omic technologies present a significant bioinformatic challenge. To address this, a data processing approach was designed to automate the comprehensive prediction of dysregulated metabolic pathways/networks from multiple data sources. The platform autonomously integrates multiple MS-based metabolomics data types without constraints due to different sample preparation/extraction, chromatographic separation, or MS detection method. This multimodal analysis streamlines the extraction of biological information from the metab"],"journal":["Analytical chemistry"],"pubmed_title":["Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology."],"pmcid":["PMC10273218"],"funding_grant_id":["P01 DA028555","DE-AC02-05CH11231","R01 GM114368","PO1 A1043376-02S1 (G.S.)","P30 MH062261","MH062261-17","R35 GM130385","R01 GM114368 (G.S.)","DA028555-09","GM114368-03"],"pubmed_authors":["Domingo-Almenara X","Phommavongsay T","Palermo A","Huan T","Ivanisevic J","Guijas C","Siuzdak G","Benton HP","Edler D","Warth B","Rinehart D"],"additional_accession":[]},"is_claimable":false,"name":"Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology.","description":"Comprehensive metabolomic data can be achieved using multiple orthogonal separation and mass spectrometry (MS) analytical techniques. However, drawing biologically relevant conclusions from this data and combining it with additional layers of information collected by other omic technologies present a significant bioinformatic challenge. To address this, a data processing approach was designed to automate the comprehensive prediction of dysregulated metabolic pathways/networks from multiple data sources. The platform autonomously integrates multiple MS-based metabolomics data types without constraints due to different sample preparation/extraction, chromatographic separation, or MS detection method. This multimodal analysis streamlines the extraction of biological information from the metab","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Jul","modification":"2026-05-28T14:21:26.259Z","creation":"2026-04-08T02:46:49.538Z"},"accession":"S-EPMC10273218","cross_references":{"pubmed":["29893550"],"doi":["10.1021/acs.analchem.8b00875"]}}