{"id":"W2036242305","doi":"10.3389/fgene.2014.00221","title":"Quantitative assessment and validation of network inference methods in bioinformatics","year":2014,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; Princess Margaret Cancer Centre","funders":"","keywords":"Computer science; Inference; Genomics; Computational genomics; Computational biology; Bioinformatics; Data science; Artificial intelligence; Genome; Biology; Genetics; Gene","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":[0.0008375358,0.0001169069,0.0002128614,0.00007999351,0.00002207616,0.00001567388,0.0001277068,0.0001443472,0.000001501505],"category_scores_gemma":[0.00005576421,0.000118265,0.00002693904,0.0001486519,0.0000903585,0.000005462934,0.0001082272,0.0001055373,2.752257e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001703267,"about_ca_system_score_gemma":0.00005104397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005153782,"about_ca_topic_score_gemma":0.00001553033,"domain_scores_codex":[0.9990187,0.0001230492,0.0004480767,0.0001319454,0.00007763398,0.000200563],"domain_scores_gemma":[0.9994884,0.00004077482,0.000171952,0.0002153965,0.0000426128,0.0000408233],"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":[0.0001199699,0.0001042451,0.5583618,0.0002507927,0.00009672473,5.197293e-7,0.001219039,0.1570379,0.008494699,0.004104789,0.003577158,0.2666323],"study_design_scores_gemma":[0.001270889,0.0007420365,0.05472376,0.00009653482,0.00002542281,0.000002137843,0.0007192854,0.9116014,0.01017016,0.01431534,0.00591428,0.0004187477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1995554,0.0008209883,0.798116,0.00002124335,0.0002686546,0.0001554155,0.000003944489,0.000002239166,0.001056046],"genre_scores_gemma":[0.3340287,0.0006984444,0.665127,0.00005688479,0.0000300103,0.000006405562,0.00003352429,0.000007126189,0.000011947],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7545635,"threshold_uncertainty_score":0.4822705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01633779366915126,"score_gpt":0.3350187110949122,"score_spread":0.3186809174257609,"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."}}