{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001779171,0.001070148,0.0006852252,0.00366026,0.0006433734,0.002799504,0.001466422,0.0009238168,0.01529457],"category_scores_gemma":[0.008359818,0.000610891,0.002191779,0.003864902,0.0005505379,0.002479488,0.003250988,0.001960681,0.00453139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553339,"about_ca_system_score_gemma":0.002902487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00607989,"about_ca_topic_score_gemma":0.009249205,"domain_scores_codex":[0.9990661,0.0002088726,0.0001989162,0.0001797983,0.000295257,0.0000511731],"domain_scores_gemma":[0.9981533,0.0009926996,0.0001306646,0.0004158888,0.0002332122,0.00007424438],"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":[0.0005696199,0.0002671893,0.009098637,0.002527565,0.0005316286,0.00284467,0.001563843,0.04123737,0.01122011,0.2009951,0.2008556,0.5282887],"study_design_scores_gemma":[0.000200279,0.00008419016,0.003478809,0.0007383233,0.0002288762,0.002197949,0.0005975488,0.203707,0.01158853,0.2726524,0.5043702,0.0001559196],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004839933,0.0005673065,0.9052329,0.00176304,0.0003500129,0.0004952665,0.04640599,0.03176501,0.008580517],"genre_scores_gemma":[0.04709392,0.001398001,0.8814235,0.000854835,0.0001201279,0.0008427236,0.06153243,0.003152923,0.003581463],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01529457,"threshold_uncertainty_score":0.05116546,"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."}}