{"id":"W3014476243","doi":"10.1101/2020.03.29.014381","title":"Computational Prediction of the Comprehensive SARS-CoV-2 vs. Human Interactome to Guide the Design of Therapeutics","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interactome; Computational biology; In silico; Coronavirus; Biology; Human proteins; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Subsequence; Protein–protein interaction; Coronavirus disease 2019 (COVID-19); Bioinformatics; Disease; Genetics; Gene; Medicine; Infectious disease (medical specialty)","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":[0.0003649261,0.0008489532,0.0008123702,0.0009062695,0.0002647281,0.0005055305,0.000375244,0.0005288853,0.002306573],"category_scores_gemma":[0.001198792,0.0002632283,0.0007954214,0.0005830072,0.0001984512,0.0003418726,0.000372115,0.0004386049,0.0003692587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003507658,"about_ca_system_score_gemma":0.0009832287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003426019,"about_ca_topic_score_gemma":0.006968457,"domain_scores_codex":[0.999884,0.00004023424,0.000006166815,0.00003052546,0.00002292308,0.00001619001],"domain_scores_gemma":[0.9996649,0.0002225001,0.00003301232,0.00001616479,0.00002493122,0.00003851714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001005834,0.0002998299,0.03058282,0.0004443928,0.0003960242,0.0006029675,0.0000564612,0.9211569,0.008723529,0.002972917,0.008375043,0.02538314],"study_design_scores_gemma":[0.00003529933,0.00006559107,0.002791208,0.000007405305,0.00003804913,0.00005320787,0.00002285057,0.9935802,0.0006978036,0.001794631,0.0009079627,0.000005785193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9368524,0.001870794,0.04084086,0.0009447644,0.00004493617,0.0001265001,0.01331627,0.001925671,0.004077857],"genre_scores_gemma":[0.9449067,0.0007261143,0.03411833,0.0002036573,0.00003839367,0.0001438715,0.01904088,0.00009677665,0.000725197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003426019,"threshold_uncertainty_score":0.007716298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07668855979630707,"score_gpt":0.3137358441931875,"score_spread":0.2370472843968804,"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."}}