{"id":"W3136924813","doi":"10.1038/s42256-021-00304-3","title":"Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices","year":2021,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":99,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mass spectrometry; Resolution (logic); DNA sequencing; Computational biology; High resolution; Computer science; Low resolution; Peptide; Deep sequencing; Algorithm; Biology; Chemistry; Genome; Artificial intelligence; Genetics; Remote sensing; Gene; Chromatography; Geography; Biochemistry","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":[0.0001684305,0.0002027982,0.0001741762,0.00004994794,0.0002741555,0.00006368662,0.0003273282,0.0003105827,0.0002136631],"category_scores_gemma":[0.0001908512,0.0002155218,0.0001022044,0.0002018589,0.00004933552,0.0001224509,0.0001167542,0.0006897299,0.00001145414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004757427,"about_ca_system_score_gemma":0.0002646388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009834211,"about_ca_topic_score_gemma":0.0002488826,"domain_scores_codex":[0.9985903,0.00001672501,0.0003574523,0.0004576665,0.000254841,0.0003230537],"domain_scores_gemma":[0.9988819,0.0001945875,0.0001769353,0.0003165359,0.0003381969,0.00009187978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001393079,0.0002698121,0.002691227,0.0004725922,0.0001495093,0.00004661521,0.0002968074,0.07735392,0.3895546,0.4761403,0.0004934022,0.05239191],"study_design_scores_gemma":[0.0002063685,0.00002330337,0.0002207981,0.0001339353,0.00003758911,0.0001454305,0.0001383934,0.06713769,0.8190875,0.1034624,0.009041607,0.0003649613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06173601,0.001003481,0.9340945,0.001043151,0.00007242909,0.0002234619,0.0002112378,0.0001987146,0.001416979],"genre_scores_gemma":[0.6981741,0.00006692232,0.3001515,0.0004165707,0.0001555212,0.0001490598,0.0003798688,0.00002274098,0.0004837618],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6364381,"threshold_uncertainty_score":0.8788723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436533948656388,"score_gpt":0.2990784096121618,"score_spread":0.2847130701255979,"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."}}