{"id":"W2092375840","doi":"10.12688/f1000research.6140.1","title":"Procedure and datasets to compute links between genes and phenotypes defined by MeSH keywords","year":2015,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ottawa Hospital Research Institute; University of Ottawa","keywords":"Computer science; Phenotype; Set (abstract data type); Inference; Ontology; Data mining; Association rule learning; Prioritization; Fuzzy logic; Gene; Computational biology; Biology; Artificial intelligence; Genetics; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002213671,0.001323971,0.0007163297,0.003632031,0.001005869,0.001970534,0.002151477,0.001386888,0.05082515],"category_scores_gemma":[0.01534452,0.0007276426,0.001431085,0.003558988,0.0004678036,0.001530499,0.002482011,0.001524034,0.02903104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001410751,"about_ca_system_score_gemma":0.003180498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004346854,"about_ca_topic_score_gemma":0.004907112,"domain_scores_codex":[0.9983268,0.0002420144,0.0003057908,0.0004640466,0.0005392061,0.0001221499],"domain_scores_gemma":[0.9955433,0.001725985,0.0002343668,0.001243365,0.001064762,0.000188196],"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":[0.0009350962,0.000275228,0.01018313,0.001477348,0.0001962079,0.0004432231,0.0002794392,0.009129428,0.009280875,0.01735265,0.8071378,0.1433096],"study_design_scores_gemma":[0.001121309,0.00020309,0.01824732,0.0003298107,0.00009413986,0.0007548414,0.0005363459,0.08618016,0.03929056,0.05825694,0.7948213,0.0001640207],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.009940541,0.0001814831,0.1459364,0.001403488,0.0002444833,0.002563326,0.7139201,0.1150143,0.01079591],"genre_scores_gemma":[0.0217461,0.0001471005,0.2712157,0.0004545904,0.00005904569,0.005457257,0.6912958,0.004825125,0.004799273],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05082515,"threshold_uncertainty_score":0.1700271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05195907390469875,"score_gpt":0.3572628042579554,"score_spread":0.3053037303532566,"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."}}