{"id":"W3135833838","doi":"10.1109/tcbb.2021.3064630","title":"CoronaPep: An Anti-Coronavirus Peptide Generation Tool","year":2021,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Shanghai Jiao Tong University; Science and Technology Commission of Shanghai Municipality; Natural Science Foundation of Henan Province; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Coronavirus; Coronavirus disease 2019 (COVID-19); Pandemic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Severe acute respiratory syndrome coronavirus; 2019-20 coronavirus outbreak; Computer science; Computational biology; Virology; Outbreak; Biology; Medicine; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001787735,0.0001715729,0.0002392396,0.000161043,0.0003001992,0.00005136948,0.00009170621,0.000192167,0.00004907961],"category_scores_gemma":[0.00003977156,0.0001513601,0.00008477198,0.0002247376,0.0001659802,0.0002244002,0.000007174941,0.0002902495,0.00009961239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005746847,"about_ca_system_score_gemma":0.0003627769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001569374,"about_ca_topic_score_gemma":0.00003124102,"domain_scores_codex":[0.9988584,0.00006527994,0.0003914701,0.0002321072,0.0002187969,0.0002339673],"domain_scores_gemma":[0.9991278,0.0002048516,0.0000651892,0.0002600398,0.000286125,0.0000559995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000732707,0.001963811,0.01670718,0.0004157381,0.0007294659,0.0001346668,0.001778599,0.006587425,0.107116,0.002885107,0.0005527152,0.8603966],"study_design_scores_gemma":[0.007928764,0.003162194,0.0364599,0.0001493274,0.0003342631,0.002217774,0.001017474,0.7377539,0.1600549,0.008874723,0.04090625,0.001140481],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7792978,0.00009150851,0.2191442,0.0004794597,0.0002840804,0.0002086867,0.00009473483,0.00007006936,0.0003294571],"genre_scores_gemma":[0.9308133,0.00003833736,0.04319161,0.02544664,0.0001620893,0.00002513126,0.0002657384,0.00001410402,0.00004311917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8592561,"threshold_uncertainty_score":0.6172285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.072003200099455,"score_gpt":0.3668063045907243,"score_spread":0.2948031044912693,"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."}}