{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002773547,0.0001603105,0.0002184245,0.00007238504,0.0001252499,0.000006794765,0.0001632118,0.0002691186,0.00005606152],"category_scores_gemma":[0.00006483169,0.0001462583,0.0001029464,0.000109807,0.0002616514,0.000007672242,0.0001318006,0.0001254954,0.00000564399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005417049,"about_ca_system_score_gemma":0.0002234336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004143867,"about_ca_topic_score_gemma":0.000007901238,"domain_scores_codex":[0.998841,0.0000935957,0.0004207363,0.0002941506,0.00006963268,0.0002808626],"domain_scores_gemma":[0.9992751,0.00002514973,0.0001302209,0.0003190288,0.000172198,0.00007834307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007762941,0.00004471456,0.0071442,0.00002473226,0.00007619859,0.000001266665,0.00004724159,0.0007625754,0.9901155,0.0003328757,0.0000907345,0.001282382],"study_design_scores_gemma":[0.004700118,0.01298793,0.7537037,0.00023694,0.0006781333,0.001292018,0.0007918145,0.1018635,0.03561415,0.003389353,0.08222395,0.002518409],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8181221,0.001833775,0.1790486,0.00002964354,0.0004427485,0.0001700468,0.0000615651,0.00001273573,0.0002788093],"genre_scores_gemma":[0.9918846,0.0005508637,0.006777824,0.0001378376,0.0003380995,0.00001352728,0.0002375226,0.00001524508,0.00004451513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9545013,"threshold_uncertainty_score":0.5964242,"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."}}