{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00135856,0.000897,0.001070663,0.00117127,0.0004724976,0.001266159,0.001244088,0.001044427,0.002674402],"category_scores_gemma":[0.002859415,0.0008660986,0.0008739433,0.001218159,0.0007112551,0.002065573,0.001823579,0.002595197,0.001777416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008777606,"about_ca_system_score_gemma":0.001819809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002185846,"about_ca_topic_score_gemma":0.003970542,"domain_scores_codex":[0.999476,0.0001228794,0.00003111013,0.0001540438,0.0001529105,0.00006307969],"domain_scores_gemma":[0.9980174,0.0009662287,0.000157717,0.0004467621,0.000287787,0.0001239943],"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":[0.0008231788,0.0004660391,0.003666524,0.0004980722,0.0004007051,0.0002661742,0.00009922271,0.2048006,0.0829086,0.03562284,0.008989632,0.6614584],"study_design_scores_gemma":[0.00003234441,0.00004986312,0.0003804224,0.00001936434,0.00003485767,0.00007501348,0.00001220866,0.9372861,0.01784186,0.0407746,0.003472843,0.000020378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04438397,0.001204449,0.9468965,0.0006364911,0.0002129471,0.00008539464,0.0007282011,0.003119872,0.002732117],"genre_scores_gemma":[0.3808464,0.001440522,0.6065079,0.0006613187,0.0001715903,0.0001871697,0.002744614,0.0003881559,0.007052441],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002674402,"threshold_uncertainty_score":0.008946776,"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."}}