{"id":"W3201602850","doi":"10.17762/de.vi.4293","title":"Sars-Cov-2 Spike protein function prediction using a convolutional neural network ensemble","year":2021,"lang":"en","type":"article","venue":"Design Engineering","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Spike (software development); Focus (optics); Machine learning; Artificial neural network; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Function (biology); Protein sequencing; Field (mathematics); Protein function prediction; Coronavirus disease 2019 (COVID-19); Computational biology; Peptide sequence; Protein function; Biology; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006754706,0.0009064698,0.0006376432,0.0006528809,0.0002906019,0.0004644501,0.0007212806,0.0008252807,0.001088394],"category_scores_gemma":[0.0009662082,0.0002812733,0.0007042566,0.0004754132,0.0002092403,0.000660709,0.000509746,0.0008706818,0.0003980077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008723347,"about_ca_system_score_gemma":0.0008686938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01837635,"about_ca_topic_score_gemma":0.01577827,"domain_scores_codex":[0.999774,0.00003634371,0.00001231514,0.00007142241,0.00004940826,0.00005647295],"domain_scores_gemma":[0.9997116,0.00009352718,0.00002401638,0.00003605134,0.0001107182,0.00002409479],"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":[0.0003550327,0.0002699108,0.008168898,0.00004104442,0.0001871554,0.0001343512,0.0000230118,0.7803897,0.01012618,0.0008143354,0.003920189,0.1955703],"study_design_scores_gemma":[0.000001767547,0.000009968024,0.0002800355,0.000001201004,0.000005555342,0.00000418691,0.00000115834,0.998769,0.0007361553,0.0001234814,0.00006582705,0.000001652614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.613574,0.001268476,0.3741438,0.00109307,0.0002089834,0.00008852053,0.001138557,0.003292723,0.005191898],"genre_scores_gemma":[0.9573743,0.0002641802,0.03652351,0.0001699466,0.00005059124,0.00004043202,0.001782853,0.00004091541,0.003753277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01837635,"threshold_uncertainty_score":0.03653878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02026361042058703,"score_gpt":0.2315139188025593,"score_spread":0.2112503083819722,"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."}}