{"id":"W3211554243","doi":"10.1093/bib/bbab455","title":"Integrative COVID-19 biological network inference with probabilistic core decomposition","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Saskatchewan; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Computational biology; In silico; Inference; Probabilistic logic; Pipeline (software); Drug repositioning; Gene; Data mining; Artificial intelligence; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006969931,0.0001981336,0.0002708699,0.000085517,0.0001348162,0.0002391652,0.0004903131,0.00008804001,0.00001420173],"category_scores_gemma":[0.002436542,0.0001514983,0.00004769309,0.001109923,0.0001516559,0.0006230308,0.0003769163,0.000257215,0.00001651827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002319341,"about_ca_system_score_gemma":0.0008896128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003683242,"about_ca_topic_score_gemma":0.00006732403,"domain_scores_codex":[0.9984235,0.0001869152,0.0004781933,0.0002878224,0.0002979734,0.0003255635],"domain_scores_gemma":[0.9971228,0.001890164,0.000192362,0.0003493233,0.0002916727,0.0001536895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004621774,0.0001174592,0.003623493,0.0001433657,0.00002209924,0.00007546274,0.003677578,0.3557756,0.00001289986,0.6277942,0.0009377138,0.007773928],"study_design_scores_gemma":[0.0005769688,0.0001561277,0.009156566,0.0002158809,0.000005211394,0.0001456626,0.0001594332,0.8247085,0.00008082329,0.1622042,0.002259575,0.000331006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02867195,0.0000519764,0.9678951,0.00105127,0.0001390292,0.0002486269,0.000007263439,0.0001255251,0.001809241],"genre_scores_gemma":[0.1546949,0.00002520301,0.8394659,0.005654815,0.00003768647,0.00003563031,0.00006628304,0.00000644534,0.00001316476],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.468933,"threshold_uncertainty_score":0.617792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05741513673907342,"score_gpt":0.3533493352080903,"score_spread":0.2959341984690169,"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."}}