{"id":"W2795167779","doi":"","title":"De novo Peptide Sequencing by Deep Learning","year":2018,"lang":"en","type":"article","venue":"Research in Computational Molecular Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Computer science; Deep learning; Computational biology; Artificial intelligence; Evolutionary biology; Biology","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":[0.00149156,0.000141433,0.0001350704,0.000210966,0.0001821932,0.000045074,0.0003423996,0.0002212427,0.00004457362],"category_scores_gemma":[0.0008353475,0.0001493389,0.00005092261,0.0003191206,0.0004382801,0.000004912602,0.0002847795,0.000534881,0.00007750485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000129304,"about_ca_system_score_gemma":0.0002644866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000607623,"about_ca_topic_score_gemma":0.00002103779,"domain_scores_codex":[0.997759,0.0007214798,0.0002806756,0.0003592165,0.0002526918,0.0006268789],"domain_scores_gemma":[0.9991515,0.000146584,0.00006530913,0.0002024514,0.0003171786,0.0001169392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001243784,0.00006846622,0.0280297,0.00004769241,0.0000735924,0.00003767583,0.0002948294,0.04871773,0.8992134,0.01248443,0.001010296,0.009897829],"study_design_scores_gemma":[0.004078637,0.006190093,0.006264495,0.0001692642,0.00001722264,0.0008082099,0.0008206522,0.5900488,0.1164312,0.09001388,0.1834906,0.001666874],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6810943,0.00034818,0.3109397,0.000514739,0.0000563706,0.0001826995,0.000008159899,0.00002259102,0.006833219],"genre_scores_gemma":[0.971718,0.00002150033,0.02696141,0.000450325,0.0001640046,0.00002514606,0.0004598084,0.00002566602,0.0001741237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7827821,"threshold_uncertainty_score":0.6089864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02721355042842543,"score_gpt":0.3929099923652853,"score_spread":0.3656964419368599,"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."}}