{"id":"W3199268518","doi":"10.1038/s41598-021-97669-7","title":"Deep neural network for detecting arbitrary precision peptide features through attention based segmentation","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"National Key Research and Development Program of China; University of Waterloo; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Segmentation; Artificial neural network; Deep neural networks; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007544012,0.0001293706,0.0001090457,0.00002803719,0.0004174356,0.0002262513,0.00008199961,0.0001056803,0.0000166142],"category_scores_gemma":[0.0004820423,0.0001246605,0.0001448378,0.0001965825,0.00005653065,0.00001932273,0.00007218782,0.0001037486,0.000002228162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001760344,"about_ca_system_score_gemma":0.00008514941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003939418,"about_ca_topic_score_gemma":0.0000386276,"domain_scores_codex":[0.9985239,0.0000675192,0.0003873112,0.0004962959,0.0002605517,0.0002644595],"domain_scores_gemma":[0.9988663,0.00003155144,0.0003147541,0.0005267193,0.0002128891,0.00004782551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007703275,0.00008947772,0.01343684,0.0001715634,0.00005138664,0.00004478099,0.0002218302,0.1118763,0.8280247,0.00003771748,0.03150751,0.01446093],"study_design_scores_gemma":[0.0008536138,0.0002420207,0.01042122,0.0001168287,0.00009671882,0.0007295962,0.000379794,0.1228894,0.7935768,0.004310258,0.06572559,0.0006581754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.765118,0.0005406102,0.2256936,0.0001362504,0.005727849,0.0005257739,0.000003472545,0.00005029535,0.002204146],"genre_scores_gemma":[0.842921,0.000004139129,0.1517733,0.000300204,0.000406623,0.00004983371,0.002223557,0.00002751633,0.002293811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07780299,"threshold_uncertainty_score":0.508351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050116485078224,"score_gpt":0.2745427136113076,"score_spread":0.2640415487605254,"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."}}