{"id":"W2008472008","doi":"10.12688/f1000research.4431.1","title":"ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis","year":2014,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"National Institutes of Health; Genome Canada","keywords":"Computer science; Context (archaeology); Biological network; Suite; Biological pathway; Computational biology; Gene regulatory network; Data mining; Bioinformatics; Biology; Gene; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003348191,0.0003572605,0.0005095789,0.0001439484,0.0002693612,0.0002902769,0.002008334,0.0006463439,0.00002878494],"category_scores_gemma":[0.0002399889,0.0002629468,0.0002605875,0.0002798871,0.0002633845,0.000004881947,0.003853459,0.0007129042,0.000009650304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002247506,"about_ca_system_score_gemma":0.0003818538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008284014,"about_ca_topic_score_gemma":0.0002476392,"domain_scores_codex":[0.99723,0.0002162012,0.0004880035,0.0009381915,0.0003956637,0.0007319418],"domain_scores_gemma":[0.9955063,0.0003030645,0.0002547979,0.003512498,0.000222431,0.0002008863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002070992,0.0003961152,0.009738785,0.002266929,0.01142492,0.00001395216,0.0003009721,0.07100014,0.01787717,0.00202519,0.7073618,0.175523],"study_design_scores_gemma":[0.000765068,0.0002413813,0.00234581,0.00006107695,0.0005455745,0.000003945653,0.00003294941,0.3807147,0.0007951464,0.001889164,0.6119754,0.0006298693],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2014393,0.02378683,0.7322113,0.009516734,0.001927707,0.01039436,0.01044749,0.0002389829,0.01003725],"genre_scores_gemma":[0.9627391,0.001217412,0.006640274,0.0009830187,0.002442819,0.0003655558,0.02373376,0.000109184,0.001768881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7612998,"threshold_uncertainty_score":0.9999823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04223566747485158,"score_gpt":0.3206499843734001,"score_spread":0.2784143168985485,"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."}}