{"id":"W2615220668","doi":"10.1093/bib/bbx052","title":"Large-scale data-driven integrative framework for extracting essential targets and processes from disease-associated gene data sets","year":2017,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"African Institute for Mathematical Sciences; International Development Research Centre","keywords":"Scale (ratio); Computer science; Computational biology; Data mining; Disease; Data science; Biology; Medicine; Geography; Cartography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002683617,0.001542595,0.001466325,0.00265045,0.001017755,0.002423132,0.002050347,0.001107119,0.001344191],"category_scores_gemma":[0.004596352,0.0007071095,0.004311745,0.00224709,0.0009186342,0.001304678,0.002171193,0.002697373,0.0004301581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001568691,"about_ca_system_score_gemma":0.00342696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01636971,"about_ca_topic_score_gemma":0.0310406,"domain_scores_codex":[0.9988844,0.0003857906,0.0001135681,0.0002861585,0.0002559402,0.00007413363],"domain_scores_gemma":[0.9980271,0.001362578,0.0001009428,0.0001624726,0.0002021632,0.0001447014],"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":[0.0002200911,0.000250047,0.008316244,0.0007860513,0.0007710852,0.0007722962,0.0002631356,0.8791667,0.006468355,0.01814859,0.00501095,0.07982643],"study_design_scores_gemma":[0.00002159883,0.00002530841,0.0007455658,0.00001895888,0.00006986844,0.00005592002,0.0000402376,0.9810491,0.0007316875,0.01429242,0.002931799,0.00001763368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01589899,0.00110576,0.9742056,0.0009261793,0.00006202971,0.0002226712,0.003274536,0.003627194,0.0006770183],"genre_scores_gemma":[0.1929557,0.001665724,0.783691,0.0004644561,0.0001294889,0.0008157198,0.01915493,0.0003936328,0.0007293369],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01636971,"threshold_uncertainty_score":0.03254884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02709551879994251,"score_gpt":0.3042514605742575,"score_spread":0.277155941774315,"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."}}