{"id":"W1971378925","doi":"10.1016/j.jcss.2004.12.001","title":"An effective algorithm for peptide de novo sequencing from MS/MS spectra","year":2005,"lang":"en","type":"article","venue":"Journal of Computer and System Sciences","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bioinformatics Solutions (Canada); Western University","funders":"","keywords":"Computer science; Task (project management); Proteomics; Algorithm; Dynamic programming; Sequence (biology); Identification (biology); Computational biology; Biology; Genetics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004180346,0.00007939072,0.000171738,0.00004084391,0.0001830599,0.0001057391,0.0002384005,0.00003881084,0.000004992321],"category_scores_gemma":[0.000002900614,0.00006018259,0.00005818299,0.00005845001,0.00006081589,0.000253329,0.0000176378,0.00009410628,4.251355e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001222134,"about_ca_system_score_gemma":0.00005444988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002198713,"about_ca_topic_score_gemma":0.000003877958,"domain_scores_codex":[0.9993173,0.00001509141,0.0002527542,0.0001439821,0.0001316088,0.0001392683],"domain_scores_gemma":[0.9994099,0.0001190522,0.0002237979,0.0000835211,0.00007552435,0.00008825518],"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":[0.00003772025,0.0001226727,0.00254538,0.0001706229,0.00009223873,0.00002933635,0.002476485,0.006703601,0.3089518,0.005749305,0.0003512704,0.6727696],"study_design_scores_gemma":[0.0007681224,0.0004526713,0.0005039934,0.000538519,0.00004281736,0.001038092,0.0008540404,0.7067152,0.2804042,0.005996819,0.002390878,0.0002946489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3476852,0.000112926,0.6516023,0.0000504739,0.00002876141,0.00006887359,0.000009590786,0.00001819492,0.0004237509],"genre_scores_gemma":[0.4871436,0.000007848133,0.5120805,0.00002278355,0.000727034,0.000007488632,4.361777e-7,0.000003148722,0.000007131703],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7000116,"threshold_uncertainty_score":0.2454175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323774681571736,"score_gpt":0.2886852760603667,"score_spread":0.2754475292446493,"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."}}