{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008668386,0.0001754148,0.0001776348,0.00005429117,0.0006761714,0.0001277443,0.00125057,0.0002965984,0.00005477453],"category_scores_gemma":[0.001411016,0.0001201923,0.00003912202,0.0001985503,0.001698062,0.00001107261,0.001765061,0.0004580458,0.00006093585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001575808,"about_ca_system_score_gemma":0.000227279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004876916,"about_ca_topic_score_gemma":0.00009559209,"domain_scores_codex":[0.9975688,0.0004548088,0.0002374156,0.0007315223,0.0002738235,0.0007335616],"domain_scores_gemma":[0.998064,0.0001475815,0.00005290384,0.001336415,0.000209062,0.000190083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002912288,0.0003060494,0.02580002,0.000145851,0.0003526431,0.0000477921,0.0005550509,0.000001071539,0.5264331,0.03028969,0.08851869,0.3272588],"study_design_scores_gemma":[0.001252081,0.001424091,0.01250467,0.00005967985,0.00004450161,0.00001439713,0.0008876807,0.0006691181,0.003301591,0.08985853,0.8893753,0.0006083354],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.960087,0.01476766,0.003699498,0.01105463,0.0002824945,0.001001318,0.00006236928,0.00008884018,0.008956204],"genre_scores_gemma":[0.9946983,0.0006463717,0.002802992,0.0004191082,0.0003253478,0.00008701981,0.0002987936,0.00003495775,0.0006870341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8008566,"threshold_uncertainty_score":0.6256579,"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."}}