{"id":"W2134974358","doi":"10.1186/gm77","title":"Linking genes to diseases: it's all in the data","year":2009,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital","funders":"Medical Research Council; South African Medical Research Council","keywords":"Computational biology; Human genetics; Gene; Disease; Gene prediction; Biological data; Identification (biology); Gene Annotation; Phenotype; Clinical phenotype; Systems biology; Human genome; Candidate gene; Genomics; Computational model; Bioinformatics; Annotation; Genome; Biology; Genetics; Computer science; Medicine; 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":[0.0004543498,0.0001158366,0.0001315877,0.00003935202,0.00004352129,0.00001673255,0.0008181817,0.00006271695,0.00001749926],"category_scores_gemma":[0.00003737167,0.00007396204,0.0000207201,0.000114766,0.00003704545,0.000003325145,0.0001685535,0.0000846015,0.00001670441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000757717,"about_ca_system_score_gemma":0.00002764201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001060784,"about_ca_topic_score_gemma":0.00003779986,"domain_scores_codex":[0.9991246,0.00002922324,0.0002381506,0.0002324551,0.0001335469,0.0002419652],"domain_scores_gemma":[0.9989668,0.00001205351,0.00004449454,0.0008705134,0.00001954526,0.0000866597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000281307,0.0003130008,0.002970498,0.0001047917,0.0001610118,0.00008931993,0.007258059,0.001836231,0.1854614,0.0008565388,0.2708298,0.5298381],"study_design_scores_gemma":[0.0005792509,0.0004451486,0.01179455,0.00003416611,0.00003331522,0.00001898817,0.0005043646,0.000453142,0.00005713748,0.0004844634,0.9854218,0.0001736862],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6399743,0.07354735,0.04735201,0.2018903,0.001610973,0.003209177,0.0003506976,0.00007275786,0.03199242],"genre_scores_gemma":[0.9600461,0.0008429488,0.0006147643,0.03540219,0.001482219,0.000007228609,0.00136845,0.000008964872,0.0002271722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.714592,"threshold_uncertainty_score":0.3016084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03455887734347834,"score_gpt":0.3007013681612952,"score_spread":0.2661424908178168,"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."}}