{"id":"W2023666978","doi":"10.1534/g3.111.000828","title":"A Resource of Quantitative Functional Annotation for<i>Homo sapiens</i>Genes","year":2012,"lang":"en","type":"article","venue":"G3 Genes Genomes Genetics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University of Toronto","funders":"National Human Genome Research Institute; National Institute of Diabetes and Digestive and Kidney Diseases; Canada Excellence Research Chairs, Government of Canada; National Heart, Lung, and Blood Institute; National Institutes of Health; National Institute of Mental Health; Canadian Institute for Advanced Research","keywords":"Annotation; Exploit; Human genome; Homo sapiens; Computational biology; Gene Annotation; Gene; Genome; Biology; Gene ontology; Computer science; Genetics; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001325241,0.002199984,0.001197531,0.005082679,0.0009767667,0.001206034,0.001495994,0.0009329253,0.02053248],"category_scores_gemma":[0.004149127,0.0007204802,0.001151994,0.005835381,0.0003410126,0.001094404,0.001509457,0.001087132,0.01285009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008798958,"about_ca_system_score_gemma":0.001905608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003671134,"about_ca_topic_score_gemma":0.005500067,"domain_scores_codex":[0.999241,0.0001169171,0.0001160168,0.0002410952,0.0002192243,0.00006581427],"domain_scores_gemma":[0.9981616,0.0006756769,0.0003252977,0.0003632336,0.0003012862,0.0001729394],"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":[0.00333527,0.0004316187,0.01977778,0.01132268,0.000640273,0.002744783,0.001234636,0.008189594,0.2463153,0.01182176,0.4769707,0.2172157],"study_design_scores_gemma":[0.0007854021,0.0004831451,0.08547723,0.00137289,0.0006709072,0.003423894,0.0004608831,0.03614768,0.09626555,0.0245007,0.7499098,0.0005018493],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02097098,0.001364724,0.07177426,0.0002567275,0.0001332007,0.0002697119,0.8372325,0.05794286,0.01005516],"genre_scores_gemma":[0.03009662,0.0007075013,0.1071473,0.0001421444,0.00004838159,0.000553745,0.8542063,0.004558773,0.002539171],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02053248,"threshold_uncertainty_score":0.06868804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02279663358286741,"score_gpt":0.2572501496883886,"score_spread":0.2344535161055212,"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."}}