{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001219561,0.0001234821,0.0002412439,0.0001897009,0.0002236631,0.0000565388,0.0004658193,0.0002502434,0.00001923097],"category_scores_gemma":[0.00003054586,0.0001128519,0.00009234799,0.0005414398,0.0002173947,0.0000130582,0.0004592372,0.0001803322,9.026177e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002055006,"about_ca_system_score_gemma":0.00004904777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009023766,"about_ca_topic_score_gemma":0.0001165361,"domain_scores_codex":[0.9988018,0.00008342182,0.0003288429,0.0003280127,0.0001495578,0.0003084194],"domain_scores_gemma":[0.9989569,0.0000306344,0.0001307809,0.0004495785,0.0003306525,0.0001015239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005437488,0.001112115,0.04991775,0.0002077654,0.007881611,0.000002064588,0.004036422,0.03250431,0.4152561,0.02322912,0.02277614,0.4376391],"study_design_scores_gemma":[0.0008479755,0.001120658,0.01113393,0.00001881982,0.0002362648,0.000002008086,0.000408537,0.9736935,0.008235943,0.001038446,0.003048253,0.0002156126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2414866,0.000466955,0.7522655,0.00002283478,0.00003142025,0.001213719,0.0000474887,0.00001252779,0.004452954],"genre_scores_gemma":[0.9950942,0.0002501571,0.003205945,0.00005008396,0.0001483488,0.000218383,0.0006318237,0.00002358519,0.00037752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9411892,"threshold_uncertainty_score":0.4601966,"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."}}