{"id":"W2963322438","doi":"10.1002/cphg.92","title":"Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics","year":2019,"lang":"en","type":"article","venue":"Current Protocols in Human Genetics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Power Generation","funders":"National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Phenotype; Computational biology; Computer science; Ontology; Bioinformatics; 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":[0.000237352,0.0001804056,0.000207028,0.00003063957,0.0001315732,0.00005774332,0.0007816594,0.0001064775,0.00002674316],"category_scores_gemma":[0.00005265855,0.0001330203,0.00006718747,0.00004138884,0.000160903,0.00000360598,0.0004111361,0.0001585592,0.000004371409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001189525,"about_ca_system_score_gemma":0.0001271903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001696194,"about_ca_topic_score_gemma":0.00006735148,"domain_scores_codex":[0.9985816,0.00009656304,0.0003838913,0.0005338423,0.0001365819,0.000267469],"domain_scores_gemma":[0.9988001,0.00008597315,0.0001683452,0.00077009,0.0001098606,0.00006565514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007479524,0.00305559,0.9131647,0.0008590069,0.0003520703,0.000009415949,0.0002820495,0.008426175,0.02285797,0.009444726,0.01827401,0.02252637],"study_design_scores_gemma":[0.01580899,0.005925599,0.4548044,0.0004624921,0.00027744,0.00001770795,0.0001946532,0.01082822,0.002845054,0.005564323,0.5014172,0.001853926],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676892,0.0004036508,0.005882272,0.00004929625,0.0002396159,0.02546356,0.0001857504,0.000008565765,0.00007807925],"genre_scores_gemma":[0.989424,0.00003738241,0.0005941243,0.00007389062,0.0005659314,0.007494558,0.001735836,0.00003171681,0.00004259873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4831432,"threshold_uncertainty_score":0.542441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010043612598541,"score_gpt":0.4269081051529444,"score_spread":0.3259037438930903,"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."}}