{"id":"W3146305827","doi":"10.7717/peerj.11117","title":"Multi-schema computational prediction of the comprehensive SARS-CoV-2 vs. human interactome","year":2021,"lang":"en","type":"article","venue":"PeerJ","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Carleton University","keywords":"Interactome; Computational biology; Pairwise comparison; Biology; Confidence interval; Bioinformatics; Computer science; Genetics; Medicine; Artificial intelligence; Gene","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":[0.0009944078,0.0009890344,0.000665631,0.001175012,0.0003901653,0.0006409617,0.0007207374,0.0007145753,0.002017514],"category_scores_gemma":[0.002509066,0.0002920564,0.00124679,0.001068326,0.0003605155,0.0005911063,0.0006545734,0.0006678435,0.000335074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004856107,"about_ca_system_score_gemma":0.001139995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004679374,"about_ca_topic_score_gemma":0.007675322,"domain_scores_codex":[0.9995642,0.0001417838,0.00002801212,0.000146804,0.00008130754,0.00003795611],"domain_scores_gemma":[0.998746,0.000897713,0.00009737322,0.00007667165,0.0001156721,0.00006639517],"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":[0.0007820586,0.0002970619,0.0479852,0.0004643598,0.0005016954,0.0006717631,0.00008841341,0.9060691,0.007605956,0.002779604,0.005745267,0.02700959],"study_design_scores_gemma":[0.00002743665,0.00007353024,0.004648774,0.000008228861,0.00005825381,0.00009922488,0.00003849149,0.9916124,0.001050196,0.001754779,0.0006210389,0.00000761998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9028974,0.00105536,0.07165435,0.0006356612,0.00003526724,0.000192234,0.01820178,0.002250451,0.003077484],"genre_scores_gemma":[0.8932013,0.0004427249,0.06409717,0.0002209693,0.00003221411,0.0001998003,0.04118146,0.0001069047,0.0005173355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004679374,"threshold_uncertainty_score":0.009304285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02916797197071512,"score_gpt":0.2863720622244643,"score_spread":0.2572040902537491,"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."}}