{"id":"W4327905722","doi":"10.20944/preprints202303.0355.v1","title":"Integrating Multi-Omics Analysis for Enhanced Diagnosis and Treatment of Glioblastoma: A Comprehensive Data-Driven Approach","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Research Institute in Oncology and Hematology; Children's Hospital Research Institute of Manitoba","funders":"","keywords":"Gene; Disease; Glioblastoma; Temozolomide; microRNA; Cancer research; Biology; Computational biology; Bioinformatics; Medicine; Genetics; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002140127,0.0003854591,0.0007442293,0.0001517533,0.00008025944,0.00002399115,0.0005702441,0.000433574,0.000005833203],"category_scores_gemma":[0.00009273645,0.0003600089,0.0003392174,0.0001496507,0.0001256919,0.000005035964,0.002249879,0.0001685983,0.000006328519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004513906,"about_ca_system_score_gemma":0.0000938661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001543454,"about_ca_topic_score_gemma":0.0002678669,"domain_scores_codex":[0.9978911,0.00005915249,0.0006431514,0.001010091,0.0001093928,0.0002870832],"domain_scores_gemma":[0.9974256,0.00007835892,0.0005340966,0.001668397,0.0001922967,0.0001012112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001417459,0.002581596,0.4924507,0.002544469,0.03448585,0.000006025591,0.007655883,0.2543863,0.1769248,0.0001684987,0.000396128,0.02698228],"study_design_scores_gemma":[0.004170076,0.0007865904,0.07701991,0.0002076722,0.004322087,0.000005731234,0.002038482,0.654987,0.2497883,0.0002970459,0.004695805,0.00168124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8939764,0.0004762853,0.1027844,0.00003637176,0.0001621431,0.001281216,0.001180786,0.00002873205,0.00007367101],"genre_scores_gemma":[0.9692667,0.002694889,0.02253512,0.00002633934,0.000156793,0.000652337,0.004437693,0.0000461935,0.0001839188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4154308,"threshold_uncertainty_score":0.9998852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1634636827026011,"score_gpt":0.3577842962775545,"score_spread":0.1943206135749534,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}