{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009957369,0.0006133763,0.0008842381,0.001059169,0.0005381397,0.001636633,0.0003538256,0.0004700728,0.001894172],"category_scores_gemma":[0.0009961488,0.0002395329,0.0009944438,0.0008554902,0.0004317155,0.0007552595,0.00104836,0.001050172,0.0007920722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005477734,"about_ca_system_score_gemma":0.001099346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008426344,"about_ca_topic_score_gemma":0.002604026,"domain_scores_codex":[0.9995276,0.00009354216,0.000032378,0.0001689016,0.0001050459,0.00007243663],"domain_scores_gemma":[0.9996206,0.0001248062,0.00006804906,0.00005245501,0.00009302659,0.00004109929],"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":[0.001344489,0.0003383014,0.0612917,0.002849373,0.0008284307,0.0006036263,0.0005118888,0.01658797,0.736711,0.009200696,0.01208618,0.1576464],"study_design_scores_gemma":[0.000227267,0.002103377,0.3192239,0.00114123,0.001946432,0.001758609,0.002026078,0.1347599,0.2025696,0.06445537,0.2694665,0.0003216899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7620853,0.03836238,0.09858598,0.004501492,0.0007486316,0.00033409,0.07175313,0.002173883,0.02145508],"genre_scores_gemma":[0.8681707,0.01330092,0.05327797,0.001938935,0.0002482467,0.0003215181,0.05935277,0.0004713533,0.00291751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001894172,"threshold_uncertainty_score":0.006336629,"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."}}