{"id":"W2124649441","doi":"10.1186/gb-2008-9-s1-s4","title":"GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function","year":2008,"lang":"en","type":"article","venue":"Genome biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1126,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"Biology; Computational biology; Function (biology); Gene regulatory network; Human genetics; Association (psychology); Genome Biology; Evolutionary biology; Gene; Genetics; Genomics; Genome; Gene expression","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":[0.0004353783,0.0001844569,0.0002183156,0.00003850844,0.0002994471,0.0000139873,0.0001332101,0.0004517361,0.00001872414],"category_scores_gemma":[0.00006339439,0.0001787184,0.0001289876,0.00008665358,0.00004066091,0.000006259329,0.00007792932,0.00009610926,0.00003201809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007958911,"about_ca_system_score_gemma":0.00006684958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002580357,"about_ca_topic_score_gemma":0.00002841211,"domain_scores_codex":[0.9987239,0.00006151933,0.0003735766,0.000335329,0.00006388839,0.0004418254],"domain_scores_gemma":[0.9992038,0.00004786093,0.0002765525,0.0002407336,0.000160339,0.00007072429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002117069,0.0000663047,0.01054906,0.00001249424,0.0003810769,0.00000106192,0.0001758353,0.001470621,0.9281273,0.0001168836,0.01003943,0.04884818],"study_design_scores_gemma":[0.0108592,0.008285293,0.08195118,0.00004284485,0.0005220113,0.0002727337,0.0002483804,0.4064308,0.03813472,0.006430278,0.4437178,0.003104728],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.331507,0.0008078529,0.6648329,0.00007301554,0.0009113053,0.000742164,0.0001915634,0.00006484502,0.0008693589],"genre_scores_gemma":[0.8788267,0.001604026,0.0972643,0.0007324044,0.007978602,0.0003136304,0.009566649,0.00008280051,0.003630866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8899927,"threshold_uncertainty_score":0.7287925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008372168286564815,"score_gpt":0.2083843575728123,"score_spread":0.2000121892862474,"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."}}