{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006849372,0.0002757487,0.0003645105,0.00009166849,0.00010252,0.0001255686,0.0005240307,0.0009622896,0.000008647157],"category_scores_gemma":[0.0005108527,0.0002378012,0.00003518784,0.00009458807,0.0003582204,0.000002140372,0.003415797,0.0006938116,0.00001225425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001624529,"about_ca_system_score_gemma":0.0002817476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001072491,"about_ca_topic_score_gemma":0.00004414872,"domain_scores_codex":[0.9979727,0.0001385887,0.0002293073,0.0008472521,0.0003706707,0.0004415163],"domain_scores_gemma":[0.9987208,0.00007241874,0.00005543448,0.0005326424,0.0001479566,0.0004707066],"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":[0.0001655865,0.00008176609,0.01208051,0.0005485854,0.0002711222,0.00001719504,0.0001816737,0.000006979196,0.01940384,0.00003417331,0.7829774,0.1842312],"study_design_scores_gemma":[0.00119048,0.001360762,0.01144729,0.0002508282,0.00009782288,0.00003225284,0.0001599213,0.0002781696,0.02207791,0.002326894,0.95974,0.001037708],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9617277,0.02754257,0.001414279,0.004368027,0.0001812971,0.0007243592,0.003598303,0.0000715071,0.0003719467],"genre_scores_gemma":[0.9490603,0.005150794,0.02103209,0.0009423961,0.00196638,0.0002351417,0.01904724,0.0001409238,0.002424793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1831935,"threshold_uncertainty_score":0.9697253,"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."}}