{"id":"W3091081431","doi":"10.1016/j.isci.2020.101697","title":"Integrative Transcriptome Analyses Empower the Anti-COVID-19 Drug Arsenal","year":2020,"lang":"en","type":"article","venue":"iScience","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Princess Margaret Cancer Centre; Vector Institute; IntelliView Technologies (Canada); Ontario Institute for Cancer Research; University Health Network; University of Toronto; Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Natural Sciences and Engineering Research Council of Canada; Cancer Research Society","keywords":"Transcriptome; Pandemic; Coronavirus disease 2019 (COVID-19); Drug; Population; Coronavirus; Clinical trial; Medicine; Outbreak; Biology; Bioinformatics; Computational biology; Pharmacology; Disease; Virology; Infectious disease (medical specialty); Genetics; Internal medicine; Gene; Environmental health","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":[0.000487614,0.0001621626,0.0002571306,0.0001138704,0.0002619221,0.00009348511,0.0005721197,0.00003995479,0.00008518028],"category_scores_gemma":[0.001065294,0.00008589654,0.0001610607,0.001499604,0.000777151,0.0001867939,0.0000764506,0.0003931244,0.0001910159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008009232,"about_ca_system_score_gemma":0.0008737695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002929096,"about_ca_topic_score_gemma":0.00005233739,"domain_scores_codex":[0.9980069,0.0001018315,0.0002110379,0.0004707138,0.0008234092,0.0003861118],"domain_scores_gemma":[0.9991143,0.0002163774,0.00004571741,0.0003276832,0.0001175333,0.0001783372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001766557,0.0001104165,0.01909028,0.00005877562,0.000050183,0.0002578026,0.02421309,0.000009097812,0.9415931,0.0004908073,0.01212689,0.001822973],"study_design_scores_gemma":[0.001445582,0.0003977332,0.009000697,0.00005670973,0.0000890981,0.00008662123,0.01077227,0.005190334,0.3557113,0.0004021601,0.6165239,0.0003236256],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9352278,0.001095008,0.005103609,0.0491992,0.0001506008,0.0004070027,0.00001353322,0.0001129791,0.008690326],"genre_scores_gemma":[0.7856647,0.000006067882,0.0001034879,0.2139811,0.0001088128,0.00001379085,6.933602e-7,0.000008002948,0.0001133308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.604397,"threshold_uncertainty_score":0.3502759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1252374466959325,"score_gpt":0.4330689977420076,"score_spread":0.3078315510460751,"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."}}