{"id":"W3029717128","doi":"10.1093/nar/gkaa467","title":"miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology","year":2020,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":912,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Genome Canada","keywords":"Biology; Workflow; microRNA; Computational biology; Context (archaeology); Gene regulatory network; Visual analytics; Interface (matter); Visualization; Computer science; Bioinformatics; Gene; Genetics; Database; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003184681,0.002602321,0.001514452,0.004174775,0.0006019398,0.003059725,0.002555652,0.0009382255,0.0445726],"category_scores_gemma":[0.008127626,0.001421519,0.001769112,0.002305897,0.0005958757,0.003160345,0.003160742,0.002152426,0.01802154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009228394,"about_ca_system_score_gemma":0.001748276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00321131,"about_ca_topic_score_gemma":0.003827622,"domain_scores_codex":[0.9984481,0.0003930966,0.0001542661,0.0003835349,0.0005105522,0.0001104218],"domain_scores_gemma":[0.9978827,0.001211926,0.0002290524,0.0002469095,0.0002837039,0.000145647],"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":[0.001792594,0.0002443499,0.00689434,0.00544666,0.0009803755,0.0008561987,0.001811362,0.03514684,0.04024873,0.04652796,0.6383001,0.2217505],"study_design_scores_gemma":[0.0004497696,0.0002076019,0.006750339,0.0007720109,0.0002587457,0.0008782327,0.0003108729,0.2914588,0.02955248,0.1068955,0.5620387,0.000426885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006876347,0.001326005,0.4665286,0.0009238362,0.0005102171,0.0004369819,0.08644498,0.4261779,0.01077516],"genre_scores_gemma":[0.08889029,0.002458951,0.6609316,0.001139138,0.0002737406,0.003407987,0.1616137,0.06807737,0.0132072],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0445726,"threshold_uncertainty_score":0.1491102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05968823757798066,"score_gpt":0.3518531770188,"score_spread":0.2921649394408194,"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."}}