{"id":"W2147461734","doi":"10.1186/gb-2008-9-s1-s2","title":"A critical assessment of Mus musculusgene function prediction using integrated genomic evidence","year":2008,"lang":"en","type":"article","venue":"Genome biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":258,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute; Microsoft Research Asia; Gwangju Institute of Science and Technology; Ontario Genomics Institute; National Institutes of Health; Ontario Genomics; Genome Canada; National Natural Science Foundation of China; Microsoft Research; National Human Genome Research Institute; W. M. Keck Foundation; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Agriculture; Cooperative State Research, Education, and Extension Service; Canadian Institutes of Health Research; National Science Foundation","keywords":"Computational biology; Function (biology); Genome; Gene; Gene prediction; Set (abstract data type); Gene ontology; Biology; Data set; Genomics; Gene Annotation; Computer science; Data mining; Genetics; Artificial intelligence","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":[0.04732279,0.001354342,0.001040519,0.005760492,0.0009820706,0.00380683,0.001601441,0.001073244,0.0008992035],"category_scores_gemma":[0.1209136,0.0003759647,0.001003701,0.003564366,0.001346914,0.001883908,0.002447486,0.001096081,0.000314257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001586022,"about_ca_system_score_gemma":0.002000927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001858576,"about_ca_topic_score_gemma":0.002348716,"domain_scores_codex":[0.9798451,0.01052966,0.001648764,0.002717936,0.004909496,0.0003490015],"domain_scores_gemma":[0.8416283,0.132758,0.005026632,0.005809065,0.01363144,0.001146602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003257761,0.0003935841,0.5542247,0.002672786,0.003591423,0.0008551062,0.0009504724,0.1015838,0.02378913,0.004346818,0.006028954,0.2983055],"study_design_scores_gemma":[0.0003710234,0.002927243,0.2198867,0.00113395,0.003235252,0.002046497,0.001330353,0.6872244,0.05374175,0.01755779,0.0103574,0.0001876305],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8563505,0.0101926,0.1200929,0.002234448,0.0001188707,0.0003986923,0.003591316,0.001276299,0.005744422],"genre_scores_gemma":[0.9428936,0.0007635365,0.05375712,0.0002052383,0.00006063801,0.0001014823,0.001951816,0.0001050566,0.0001614981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04732279,"threshold_uncertainty_score":0.2502698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03303659567913406,"score_gpt":0.2982599705044643,"score_spread":0.2652233748253303,"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."}}