{"paper":{"title":"IntLIM: Integration using Linear Models of metabolomics and gene expression data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"q-bio.GN","authors_text":"Bofei Zhang, Carmen Z. Cantemir-Stone, Elizabeth Baskin, Ewy A. Math\\'e, Jalal K. Siddiqui, Joseph P. McElroy, Kevin R. Coombes, Mingrui Liu, Russell Bonneville","submitted_at":"2018-02-28T18:55:50Z","abstract_excerpt":"Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large cohorts, driving a need for the development of novel methods for their integration. Of note, clinical/translational studies typically provide snapshot gene and metabolite profiles and, oftentimes, most metabolites are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.10588","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}