{"id":"W2109628189","doi":"10.1142/s0219720013500078","title":"<i>DE NOVO</i> SEQUENCING WITH LIMITED NUMBER OF POST-TRANSLATIONAL MODIFICATIONS PER PEPTIDE","year":2013,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computational biology; Posttranslational modification; Computer science; Proteomics; Peptide; Sequence (biology); DNA sequencing; Tandem mass spectrometry; Biology; Identification (biology); Chemistry; Genetics; Mass spectrometry; Biochemistry; Gene; Chromatography","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":[0.001305549,0.001134605,0.0009310285,0.0007134747,0.0008204518,0.00167829,0.001839211,0.0008732862,0.004819063],"category_scores_gemma":[0.003213252,0.000795332,0.001057832,0.0008984358,0.0005580388,0.001384028,0.001343995,0.001336295,0.004885345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006689685,"about_ca_system_score_gemma":0.0009394376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001415088,"about_ca_topic_score_gemma":0.001898771,"domain_scores_codex":[0.999267,0.0001014153,0.00007385621,0.0003112815,0.0001902554,0.00005620999],"domain_scores_gemma":[0.9989139,0.0002826691,0.0001661051,0.0003276533,0.0002572102,0.00005245614],"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":[0.0008338618,0.0001517884,0.003286427,0.00106772,0.0002212162,0.0004705306,0.0002598502,0.02102988,0.4224002,0.02246347,0.0225028,0.5053123],"study_design_scores_gemma":[0.0001119697,0.0002499126,0.002407136,0.0001026429,0.00009377291,0.00151751,0.00007392271,0.3234064,0.5413509,0.02033145,0.110227,0.0001274428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008013241,0.0002606909,0.9826151,0.00009413638,0.00008614901,0.0001131115,0.0004432007,0.006202538,0.002171778],"genre_scores_gemma":[0.03481627,0.0003412077,0.958261,0.0001676202,0.00003135584,0.0001723754,0.001873976,0.001105099,0.003231168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004819063,"threshold_uncertainty_score":0.01612139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181630914459508,"score_gpt":0.2614766387186723,"score_spread":0.2496603295740772,"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."}}