{"id":"W2113765377","doi":"10.1021/pr0602085","title":"Using Annotated Peptide Mass Spectrum Libraries for Protein Identification","year":2006,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":305,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of British Columbia","funders":"National Institute of Standards and Technology","keywords":"Proteome; Sequence database; Peptide; Human proteome project; Computational biology; Peptide sequence; Tandem mass spectrometry; Computer science; Sequence (biology); Peptide library; Identification (biology); Similarity (geometry); Bioinformatics; Chemistry; Biology; Proteomics; Mass spectrometry; Artificial intelligence; Genetics; Biochemistry; Chromatography","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":[0.001208348,0.0001178515,0.0002016072,0.0002603274,0.0003252251,0.0002019927,0.0004266259,0.0001180989,0.0000926297],"category_scores_gemma":[0.0001946101,0.0001078103,0.0001200385,0.000407788,0.0001436181,0.0004249907,0.00005818306,0.0005863964,0.000005533931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002193844,"about_ca_system_score_gemma":0.0002561635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003142311,"about_ca_topic_score_gemma":0.000001771985,"domain_scores_codex":[0.998174,0.00004487856,0.0006534781,0.0002009974,0.0005296654,0.000396954],"domain_scores_gemma":[0.9984238,0.00007338594,0.000467728,0.0002939229,0.0006587754,0.00008237088],"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":[0.0001294806,0.00007663588,0.0001461772,0.0001516437,0.00001371118,0.00000600429,0.00001677878,0.0001268049,0.9902929,0.008367745,0.000441043,0.000231068],"study_design_scores_gemma":[0.0002833363,0.00005430701,0.00003887674,0.0001109005,0.000006066778,0.00002324445,0.00005320379,0.00116965,0.770771,0.2222206,0.005176186,0.00009263687],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4805582,0.0002580207,0.5140127,0.001848179,0.00002096324,0.00158889,0.00006787479,0.00006577651,0.001579377],"genre_scores_gemma":[0.5596232,0.00001903823,0.434987,0.00000384063,0.000699661,0.0004527715,0.00002039853,0.00005276072,0.004141384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.219522,"threshold_uncertainty_score":0.4396377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08519475400272278,"score_gpt":0.3943488391147021,"score_spread":0.3091540851119793,"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."}}