{"id":"W3116244508","doi":"10.1101/2020.12.23.424177","title":"Identification of COVID-19-relevant transcriptional regulatory networks and associated kinases as potential therapeutic targets","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"interferon and immune responses","field":"Immunology and Microbiology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Biology; Systems biology; Computational biology; Interferon regulatory factors; Signal transduction; Gene regulatory network; Transcriptional regulation; Identification (biology); microRNA; Kinase; Regulation of gene expression; Gene; Transcription factor; Cell biology; Gene expression; Genetics","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":[0.0002320165,0.0004728691,0.000615188,0.0003943621,0.0001553694,0.0006476877,0.0002707165,0.0002208134,0.002228057],"category_scores_gemma":[0.0002761979,0.0001471563,0.0005594845,0.0002934263,0.0001518169,0.0002952351,0.000276955,0.0004412085,0.0002723098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006284078,"about_ca_system_score_gemma":0.0005543829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005475487,"about_ca_topic_score_gemma":0.0009810895,"domain_scores_codex":[0.9999191,0.00001860842,0.000004703861,0.00002383138,0.00001962804,0.00001420456],"domain_scores_gemma":[0.9999311,0.00002272433,0.00002424804,0.000004652202,0.000007916477,0.000009410315],"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":[0.003091466,0.0007079919,0.03021366,0.001606572,0.0004018871,0.0007265826,0.00006772539,0.2511888,0.6111121,0.01028222,0.002759948,0.08784097],"study_design_scores_gemma":[0.0003674815,0.000849552,0.02059169,0.0001106202,0.000410036,0.0006147843,0.0001703742,0.7331333,0.2197201,0.0098203,0.01415976,0.00005212007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9130762,0.008393573,0.06462934,0.0007995172,0.0001012114,0.0002541499,0.005251564,0.0009210825,0.006573358],"genre_scores_gemma":[0.969741,0.002323272,0.02352934,0.0001047229,0.00002055144,0.0001178806,0.003258983,0.00003658076,0.000867648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002228057,"threshold_uncertainty_score":0.007453561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148614836357347,"score_gpt":0.2338246193981133,"score_spread":0.2189631357623786,"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."}}