{"id":"W3124723126","doi":"10.20944/preprints202008.0253.v1","title":"Prediction and Analysis of SARS-CoV-2-Targeting <i>microRNA</i> in Human Lung Epithelium","year":2020,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"","keywords":"microRNA; Coronavirus; Biology; Virology; RNA; Mechanism (biology); Coronavirus disease 2019 (COVID-19); Immunology; Gene; Genetics; Disease; Medicine; Pathology; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001742565,0.0002425446,0.0003449259,0.0003727034,0.0001866695,0.0003694621,0.0001060998,0.0003413966,0.001304246],"category_scores_gemma":[0.0005238493,0.0001609469,0.0003399947,0.0002739446,0.0001044585,0.0001192422,0.0001419527,0.0002176475,0.001497552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000161804,"about_ca_system_score_gemma":0.0002155549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006021458,"about_ca_topic_score_gemma":0.0006709395,"domain_scores_codex":[0.9998749,0.00001602354,0.000009853288,0.00005547633,0.000029169,0.00001449355],"domain_scores_gemma":[0.9998523,0.00005353037,0.00003242023,0.00001195936,0.0000306732,0.00001914518],"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":[0.0007606492,0.00006231748,0.03465307,0.0002406499,0.0000441989,0.0005918143,0.0001157054,0.002431596,0.9369589,0.0002656234,0.001041955,0.0228335],"study_design_scores_gemma":[0.00007013202,0.0007532063,0.2343755,0.000044086,0.0002000577,0.00303227,0.0002858183,0.08803087,0.6596198,0.0005617016,0.01298007,0.0000464724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755468,0.001437166,0.01576361,0.00009453221,0.00003334384,0.00005735391,0.004579902,0.0005472929,0.001940004],"genre_scores_gemma":[0.9632609,0.0004356213,0.02513163,0.0001053348,0.00001277815,0.00007859154,0.008893397,0.0001245835,0.001957203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001304246,"threshold_uncertainty_score":0.00436312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05082982092555497,"score_gpt":0.3166939297280539,"score_spread":0.2658641088024989,"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."}}