{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01800354,0.001104361,0.002168346,0.006695975,0.001736743,0.009881838,0.00302981,0.004235891,0.007759199],"category_scores_gemma":[0.110783,0.001211351,0.001787082,0.01197398,0.00549722,0.0171792,0.006824242,0.008744135,0.003461701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001123619,"about_ca_system_score_gemma":0.003500355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004051078,"about_ca_topic_score_gemma":0.002987103,"domain_scores_codex":[0.9918532,0.004039407,0.0008759376,0.001336166,0.001673473,0.000221988],"domain_scores_gemma":[0.9280595,0.04465048,0.003562147,0.0188297,0.003048602,0.001849578],"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":[0.001137843,0.0002941836,0.1032158,0.0046569,0.001830935,0.002348555,0.00485246,0.004930516,0.003161244,0.1344272,0.2641425,0.4750018],"study_design_scores_gemma":[0.0001382475,0.00009771554,0.01608771,0.00331659,0.0008339664,0.002506827,0.003147789,0.005854202,0.001104813,0.6375985,0.3290946,0.0002190078],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04177102,0.07117233,0.2591945,0.5401672,0.01180334,0.0003063534,0.05688614,0.005072068,0.01362707],"genre_scores_gemma":[0.3747212,0.06271034,0.3696535,0.1113209,0.01186533,0.0009254694,0.05939673,0.002732004,0.006674418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01800354,"threshold_uncertainty_score":0.09521294,"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."}}