{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006225193,0.0001317208,0.0002149199,0.0001427968,0.0001775716,0.00006693932,0.0001606164,0.0002171412,0.0000588653],"category_scores_gemma":[0.000260842,0.000128078,0.0001393702,0.0006615065,0.0002030714,0.000003874225,0.0001278479,0.0001308798,0.00001132724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003005446,"about_ca_system_score_gemma":0.000141672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002046468,"about_ca_topic_score_gemma":0.00001292946,"domain_scores_codex":[0.9983748,0.0002281733,0.0002305548,0.0005101067,0.0002371872,0.0004191291],"domain_scores_gemma":[0.9990164,0.0000928899,0.00005983403,0.000230372,0.000364788,0.0002356984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001270314,0.0001169625,0.1147043,0.0002100509,0.001530157,0.000003577043,0.00002818926,0.01884114,0.8219902,0.0007505704,0.03882545,0.001729078],"study_design_scores_gemma":[0.002722612,0.001877834,0.07585852,0.00002256894,0.0005649761,0.000004369277,0.0001348801,0.7570001,0.02359083,0.0002041509,0.1374091,0.0006100393],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9226905,0.001544013,0.07423354,0.0006787405,0.00007245134,0.0005191343,0.000108749,0.00002111008,0.0001318294],"genre_scores_gemma":[0.9970991,0.00004352633,0.0007596615,0.0001930758,0.0007033272,0.00005990852,0.0009716917,0.00002544755,0.0001442724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7983993,"threshold_uncertainty_score":0.5222868,"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."}}