{"id":"W2028689806","doi":"10.12688/f1000research.4572.1","title":"GeneMANIA: Fast gene network construction and function prediction for Cytoscape","year":2014,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":282,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Center for Research Resources; National Institute of General Medical Sciences; National Institutes of Health; Government of Ontario","keywords":"Construct (python library); Gene ontology; Function (biology); Computational biology; Gene regulatory network; Gene; Drosophila melanogaster; Computer science; Biology; Open peer review; Plant biology; Gene expression; Evolutionary biology; Genetics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007868023,0.0002550074,0.0002515647,0.00007534762,0.0002526495,0.0001307549,0.0002191652,0.0007190232,0.00001974665],"category_scores_gemma":[0.00003969593,0.0002595753,0.0001250271,0.00005967472,0.0001663181,0.000004161333,0.0007217518,0.0004012447,0.000008447027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003134188,"about_ca_system_score_gemma":0.0001486788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001207744,"about_ca_topic_score_gemma":0.00002221884,"domain_scores_codex":[0.998319,0.00008313693,0.0003690501,0.0005473928,0.0002181321,0.0004633324],"domain_scores_gemma":[0.9989049,0.00002952509,0.0001585356,0.0005185861,0.0002386653,0.0001498308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002990517,0.0001543325,0.009352162,0.002389629,0.001692212,0.000002801035,0.0001899092,0.07487093,0.1314923,0.004203219,0.3261677,0.4464943],"study_design_scores_gemma":[0.003907856,0.002910615,0.01029984,0.0002871523,0.0004276421,0.0001589528,0.0001981937,0.2489423,0.01799188,0.03170461,0.6815326,0.001638379],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.188951,0.003601828,0.8001164,0.0002487739,0.002517136,0.002155844,0.0004802136,0.00006515463,0.001863699],"genre_scores_gemma":[0.9105321,0.004749536,0.05014404,0.000542503,0.01738218,0.001082835,0.01259459,0.0002216515,0.002750614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7499723,"threshold_uncertainty_score":0.9999856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01344014492403231,"score_gpt":0.2543249517949349,"score_spread":0.2408848068709026,"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."}}