{"id":"W3185042382","doi":"10.1093/bioinformatics/btab527","title":"miRAnno—network-based functional microRNA annotation","year":2021,"lang":"en","type":"article","venue":"Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Discovery Centre; University Health Network","funders":"Canada Foundation for Innovation; University Health Network; International Business Machines Corporation","keywords":"Annotation; microRNA; Computer science; Computational biology; Noise (video); Data mining; Artificial intelligence; Biology; Genetics; Gene","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":[0.001755065,0.002065043,0.0009618033,0.002648453,0.0009000255,0.00147974,0.001652073,0.0007891456,0.007018592],"category_scores_gemma":[0.004175697,0.0007138076,0.001292192,0.001312365,0.000568995,0.001490717,0.002010457,0.001056419,0.005864363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000647302,"about_ca_system_score_gemma":0.001006777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001526512,"about_ca_topic_score_gemma":0.002076253,"domain_scores_codex":[0.9984832,0.0003923958,0.00008819358,0.0005668805,0.0003651937,0.0001041582],"domain_scores_gemma":[0.9988157,0.0005289272,0.000187033,0.0001771357,0.0001865047,0.0001047463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003976415,0.0003712957,0.02762568,0.008832152,0.001448825,0.001119252,0.001003678,0.07750259,0.2531369,0.04158749,0.2424736,0.3409222],"study_design_scores_gemma":[0.0001542154,0.0003413557,0.01301267,0.0004003188,0.0004111066,0.001510253,0.0001544328,0.5678986,0.1458786,0.05170643,0.2182384,0.0002936623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03525672,0.00233983,0.7166308,0.0007309242,0.0003302688,0.0004890388,0.05480991,0.1788924,0.0105202],"genre_scores_gemma":[0.2161058,0.001427234,0.6519153,0.000616058,0.0001814099,0.001703625,0.1089545,0.0117557,0.007340365],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007018592,"threshold_uncertainty_score":0.02347952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01222106977605556,"score_gpt":0.2265008037804691,"score_spread":0.2142797340044135,"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."}}