{"paper":{"title":"Integration of Gene Expression Data and Methylation Reveals Genetic Networks for Glioblastoma","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":["q-bio.GN"],"primary_cat":"cs.CE","authors_text":"Francesco Gadaleta, Kyrylo Bessonov","submitted_at":"2015-05-30T07:02:48Z","abstract_excerpt":"Motivation: The consistent amount of different types of omics data requires novel methods of analysis and data integration. In this work we describe Regression2Net, a computational approach to analyse gene expression and methylation profiles via regression analysis and network-based techniques.\n  Results: We identified 284 and 447 unique candidate genes potentially associated to the Glioblastoma pathology from two networks inferred from mixed genetic datasets. In-depth biological analysis of these networks reveals genes that are related to energy metabolism, cell cycle control (AATF), immune s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.00080","kind":"arxiv","version":2},"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"}