{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002622884,0.002957806,0.001954817,0.004403297,0.001557579,0.002358534,0.004281,0.001592042,0.07902326],"category_scores_gemma":[0.008632356,0.002239395,0.002136496,0.002567025,0.0006314652,0.0025985,0.002574087,0.003948099,0.03861281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003125,"about_ca_system_score_gemma":0.002745972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005064773,"about_ca_topic_score_gemma":0.008395811,"domain_scores_codex":[0.9990726,0.0001971154,0.00005487585,0.0002458917,0.0003459899,0.00008355845],"domain_scores_gemma":[0.9979922,0.00116528,0.0001166858,0.0003109721,0.0002423771,0.0001725728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006752178,0.0001655985,0.002530583,0.004956811,0.000941403,0.0008798886,0.000862284,0.01120214,0.01959509,0.02312476,0.8050551,0.1300112],"study_design_scores_gemma":[0.001076553,0.0001510553,0.003814699,0.0007699759,0.0004204425,0.001405135,0.0002242039,0.1765867,0.04499586,0.1142092,0.6558536,0.0004925896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.005177963,0.001100137,0.3549765,0.0005753915,0.0006346246,0.0004719846,0.1107383,0.5195207,0.00680439],"genre_scores_gemma":[0.05379964,0.001996995,0.5866347,0.0009759794,0.0002494028,0.004790534,0.2193011,0.121143,0.01110872],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.07902326,"threshold_uncertainty_score":0.2643591,"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."}}