{"id":"W4292615690","doi":"10.1093/nargab/lqac058","title":"A computational approach to rapidly design peptides that detect SARS-CoV-2 surface protein S","year":2022,"lang":"en","type":"article","venue":"NAR Genomics and Bioinformatics","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Ottawa Hospital; University of Regina; Carleton University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Computational biology; Pandemic; Peptide; Coronavirus; Surface plasmon resonance; Biology; Virology; Computer science; Medicine; Biochemistry; Disease; Nanotechnology; Infectious disease (medical specialty); Pathology; Materials science","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":[0.0006159349,0.0009147465,0.0005832815,0.0003976501,0.0004177046,0.0007308157,0.0007367883,0.0007075894,0.001610038],"category_scores_gemma":[0.001489408,0.0003383315,0.0005712594,0.000302998,0.0003785922,0.0005364026,0.0005392632,0.000755172,0.0002799117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004254855,"about_ca_system_score_gemma":0.001405878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001679496,"about_ca_topic_score_gemma":0.002889832,"domain_scores_codex":[0.9997995,0.0000570915,0.00001267089,0.00005398216,0.00005149786,0.00002522218],"domain_scores_gemma":[0.9995926,0.0002527029,0.00004096865,0.00002813377,0.00005682338,0.00002880155],"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":[0.0004478715,0.0005431324,0.007182652,0.0003140365,0.0001975811,0.0001900658,0.00009733297,0.8812737,0.03172577,0.00604818,0.001603993,0.0703756],"study_design_scores_gemma":[0.00003399854,0.0001412485,0.0001819925,0.000003902629,0.00002082191,0.00001879819,0.00001601375,0.9938793,0.003730593,0.001266961,0.0007020761,0.000004276307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3788892,0.0003947451,0.6098813,0.0005823405,0.0001140188,0.0002702425,0.0005042924,0.002312283,0.007051594],"genre_scores_gemma":[0.5835348,0.0002568231,0.4124604,0.0003729218,0.00002798004,0.0003518391,0.0009582068,0.000157874,0.001879201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001679496,"threshold_uncertainty_score":0.005386114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06575502002795491,"score_gpt":0.3012700700699599,"score_spread":0.235515050042005,"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."}}