{"id":"W2166562911","doi":"10.1093/nar/gkr1050","title":"Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks","year":2011,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Biology; Pipeline (software); Gene regulatory network; Context (archaeology); Computational biology; Data integration; Source code; Set (abstract data type); Genomics; Variety (cybernetics); Visualization; Computer science; Gene interaction; Gene; Data mining; Genome; Genetics; Gene expression; Artificial intelligence","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":[0.001753426,0.001673375,0.0009978238,0.003491859,0.0006931369,0.001537667,0.002552073,0.001013094,0.02032632],"category_scores_gemma":[0.006340886,0.001127037,0.00137964,0.003226032,0.0006512075,0.002377685,0.003807005,0.001671298,0.008437193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008505126,"about_ca_system_score_gemma":0.001810545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006775576,"about_ca_topic_score_gemma":0.008327469,"domain_scores_codex":[0.9992199,0.0001387501,0.00005665872,0.0001832311,0.0003547924,0.00004651915],"domain_scores_gemma":[0.9982601,0.001102263,0.0001240273,0.000221781,0.0001527076,0.0001392059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001406899,0.0002835551,0.008325763,0.002172669,0.0006925885,0.00182838,0.0009407079,0.07545928,0.02096746,0.03127989,0.4404847,0.4161581],"study_design_scores_gemma":[0.0005470407,0.0001354022,0.008891588,0.0003698336,0.0001921836,0.001465646,0.0001961446,0.4609932,0.02026052,0.1127347,0.3938627,0.0003510024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00535904,0.0004371621,0.5066022,0.0005662699,0.0001576869,0.0003221426,0.03883295,0.4418233,0.005899241],"genre_scores_gemma":[0.1144674,0.002774911,0.6517017,0.0009303789,0.0002599831,0.00264727,0.1633492,0.04984856,0.01402048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02032632,"threshold_uncertainty_score":0.06799829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04233630574430571,"score_gpt":0.3330370552763719,"score_spread":0.2907007495320662,"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."}}