{"id":"W2170282111","doi":"10.1093/nar/gkt1026","title":"The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data","year":2013,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":837,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Basic Energy Sciences; National Institute of Mental Health; National Institutes of Health; National Human Genome Research Institute; Bundesministerium für Bildung und Forschung; Office of Science; University College London; British Heart Foundation; National Institute for Health and Care Research; Deutsche Forschungsgemeinschaft; U.S. Department of Energy","keywords":"Unified Medical Language System; Annotation; Ontology; DECIPHER; Controlled vocabulary; Documentation; Computer science; Biology; Phenotype; Set (abstract data type); Interoperability; UniProt; Computational biology; Function (biology); Information retrieval; Bioinformatics; World Wide Web; Genetics","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":[0.006029323,0.001503717,0.0009770232,0.007936439,0.001073456,0.002432349,0.001864293,0.001245671,0.007082968],"category_scores_gemma":[0.01062316,0.0007680214,0.001633633,0.009203642,0.001106945,0.004292832,0.005159663,0.001968952,0.002835512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001518155,"about_ca_system_score_gemma":0.0081569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0122747,"about_ca_topic_score_gemma":0.007690267,"domain_scores_codex":[0.9971335,0.0008979242,0.0006435984,0.0004540852,0.0007195996,0.0001512745],"domain_scores_gemma":[0.9943976,0.002487777,0.0007891236,0.001174294,0.0006366065,0.0005145737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006505281,0.0003908378,0.01618285,0.007679265,0.0008651827,0.001824534,0.003692386,0.006246996,0.02017593,0.1834766,0.4142534,0.3445615],"study_design_scores_gemma":[0.0002588813,0.00008395097,0.01758628,0.001737739,0.0003282403,0.001385192,0.0008327776,0.008713572,0.005754496,0.08019108,0.8829812,0.0001466835],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01379814,0.003377949,0.5504738,0.005216989,0.0006557177,0.002025394,0.382895,0.01806316,0.02349389],"genre_scores_gemma":[0.03480443,0.005677391,0.4497757,0.001721437,0.0002197898,0.003232804,0.4977994,0.002773077,0.00399594],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0122747,"threshold_uncertainty_score":0.03188646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09187093386330562,"score_gpt":0.4176459453405298,"score_spread":0.3257750114772242,"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."}}