{"id":"W4212958893","doi":"10.1186/s13023-022-02217-9","title":"Rare disorders have many faces: in silico characterization of rare disorder spectrum","year":2022,"lang":"en","type":"article","venue":"Orphanet Journal of Rare Diseases","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Alberta Children's Hospital; University of Calgary","funders":"Canadian Institutes of Health Research; Genome British Columbia; BC Children's Hospital; Children's Hospital Foundation; Genome Canada","keywords":"In silico; Human genetics; Computational biology; Characterization (materials science); Biology; Genetics; Nanotechnology; Materials science; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001034892,0.0002141147,0.0003218383,0.0001941375,0.0001165064,0.00002843354,0.0003928296,0.00006048891,0.0005075998],"category_scores_gemma":[0.00006783391,0.0002047576,0.0002688643,0.0001661033,0.00007154137,0.00002032517,0.000191333,0.0001380154,0.000001527886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003572446,"about_ca_system_score_gemma":0.0002314197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002202814,"about_ca_topic_score_gemma":0.00005271312,"domain_scores_codex":[0.9984882,0.000150982,0.0005217586,0.0002606384,0.0003132519,0.0002651572],"domain_scores_gemma":[0.9989767,0.00001738474,0.0004933003,0.0002660478,0.00008540271,0.0001611751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002774915,0.002621811,0.7379298,0.0003281221,0.0003816515,0.0004732335,0.0005615799,0.005846803,0.2384259,0.00009842218,0.002919283,0.007638524],"study_design_scores_gemma":[0.008071349,0.003385804,0.8156306,0.0002327035,0.0004574855,0.0004816706,0.007154543,0.0004208104,0.02487403,0.001007447,0.1367511,0.001532544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926332,0.005114715,0.0001005267,0.0002955748,0.0002966797,0.0002223538,0.001273431,0.000004918774,0.00005855989],"genre_scores_gemma":[0.9968421,0.001359761,0.00001968968,0.0001570971,0.0001423302,0.00001944042,0.001173273,0.00003605367,0.0002502845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2135518,"threshold_uncertainty_score":0.8349773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004669936726636064,"score_gpt":0.2146430095363517,"score_spread":0.2099730728097156,"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."}}