{"id":"W2099146841","doi":"10.1109/tnb.2015.2419194","title":"A Framework of De Novo Peptide Sequencing for Multiple Tandem Mass Spectra","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on NanoBioscience","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electron-transfer dissociation; Tandem mass spectrometry; Fragmentation (computing); Tandem; Chemistry; Mass spectrometry; Spectral line; Mass spectrum; Dissociation (chemistry); Peptide; Computer science; Physics; Chromatography; Materials science; Physical chemistry; 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":[0.0001779212,0.0001213612,0.0001388553,0.00005619091,0.0001653866,0.00001866618,0.0002994529,0.0001167641,0.00002862371],"category_scores_gemma":[0.00004974354,0.000123048,0.00009199538,0.0002835036,0.0001464275,0.0001103958,0.000001289617,0.000187476,0.000003944373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002839596,"about_ca_system_score_gemma":0.0002242274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002931344,"about_ca_topic_score_gemma":0.00001387001,"domain_scores_codex":[0.9990507,0.000006062584,0.0002183321,0.0002894467,0.0001614068,0.0002740924],"domain_scores_gemma":[0.9991601,0.0001697912,0.0001012658,0.0003413295,0.0001001624,0.0001273707],"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":[0.00002459872,0.00006083909,0.00006833307,0.00002159721,0.00000434124,7.238992e-7,0.0001878721,0.004263057,0.9937654,0.0008160844,0.00001765572,0.0007695199],"study_design_scores_gemma":[0.0002512425,0.00005618641,0.000002271172,0.00006983152,0.00001181264,0.00001142145,0.0002148544,0.005802397,0.9809472,0.01186422,0.0006286626,0.0001399146],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0924044,0.0000103337,0.9063424,0.0001160228,0.00005974156,0.0002057834,0.0000979991,0.0001239042,0.0006393612],"genre_scores_gemma":[0.6394261,0.0000129148,0.3600358,0.00004583813,0.00002045436,0.0001617932,7.595152e-7,0.00001173321,0.0002846088],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5470217,"threshold_uncertainty_score":0.5017751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03941688970829456,"score_gpt":0.3020163390864356,"score_spread":0.2625994493781411,"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."}}