{"id":"W2034368202","doi":"10.1016/j.ygeno.2014.03.004","title":"Inference and validation of predictive gene networks from biomedical literature and gene expression data","year":2014,"lang":"en","type":"article","venue":"Genomics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Inference; Biology; Gene regulatory network; Computational biology; Gene; Biological network; Genomics; Machine learning; Computer science; Artificial intelligence; Gene expression; Bioinformatics; Data mining; Genetics; Genome","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03067705,0.001641516,0.001483359,0.008618562,0.0009860881,0.003239239,0.002784545,0.001741164,0.001066023],"category_scores_gemma":[0.1098855,0.0008152595,0.002092531,0.003903208,0.002326272,0.002999508,0.00208555,0.002829411,0.0005227363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00205882,"about_ca_system_score_gemma":0.003915266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005167881,"about_ca_topic_score_gemma":0.006118909,"domain_scores_codex":[0.983043,0.01004888,0.0007930382,0.002650284,0.00313887,0.000325879],"domain_scores_gemma":[0.9039125,0.08248398,0.004238753,0.005748488,0.003213607,0.0004026809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000503964,0.0003569712,0.03809683,0.001222497,0.001255235,0.0005434572,0.0003686816,0.7741338,0.0196959,0.02209125,0.001807422,0.1399241],"study_design_scores_gemma":[0.00002746298,0.00004586683,0.004161811,0.00009459854,0.0001060536,0.0001101791,0.00005772357,0.9569122,0.008785969,0.02854834,0.00112346,0.00002626862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04571629,0.001287231,0.9482715,0.0005570972,0.00004416184,0.000125136,0.002102048,0.001221444,0.0006751918],"genre_scores_gemma":[0.4924907,0.001395865,0.4945774,0.0003148542,0.0001029509,0.0003633972,0.01021566,0.000253887,0.0002852392],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03067705,"threshold_uncertainty_score":0.1622377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00849478376278718,"score_gpt":0.2261721980516543,"score_spread":0.2176774142888671,"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."}}