{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005722441,0.0009900352,0.0007745026,0.000686243,0.0003163331,0.0006261506,0.001357886,0.001096381,0.001479939],"category_scores_gemma":[0.001244958,0.0003891117,0.0006081165,0.0007668227,0.0005776726,0.001094482,0.0008590054,0.001291301,0.0004383334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312038,"about_ca_system_score_gemma":0.0009910974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01129434,"about_ca_topic_score_gemma":0.01054868,"domain_scores_codex":[0.9997042,0.00003948455,0.00001479609,0.000104982,0.0000629424,0.00007364433],"domain_scores_gemma":[0.9996159,0.000163066,0.000049343,0.00003484916,0.0001088204,0.00002796145],"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":[0.0004840655,0.0002618287,0.002943868,0.000111033,0.0001127792,0.0001622075,0.00009032687,0.4963284,0.03130135,0.005320277,0.005443614,0.4574403],"study_design_scores_gemma":[0.000004853864,0.00002229888,0.0001740345,0.000003457645,0.000006660775,0.00001076826,0.000003309139,0.9962474,0.002077152,0.001253944,0.0001927325,0.000003451234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1032802,0.001270818,0.8881512,0.0005209125,0.0001191287,0.00007570664,0.0003317928,0.003167087,0.003083155],"genre_scores_gemma":[0.8427704,0.0004652446,0.1475786,0.0005352028,0.00007132241,0.0001133201,0.001101813,0.0001042588,0.007259745],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01129434,"threshold_uncertainty_score":0.02245718,"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."}}