{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002510685,0.001960447,0.001506755,0.005099526,0.001489148,0.002225111,0.001807673,0.0007025138,0.007719444],"category_scores_gemma":[0.00470774,0.0008646663,0.001087767,0.004633717,0.0003095457,0.002436412,0.001419555,0.001414409,0.005572715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006769548,"about_ca_system_score_gemma":0.0008195919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007075621,"about_ca_topic_score_gemma":0.001030611,"domain_scores_codex":[0.998225,0.000344717,0.0002763428,0.0004190545,0.0006061891,0.0001287411],"domain_scores_gemma":[0.9971239,0.0008047129,0.0003029617,0.0005167027,0.001035462,0.0002162396],"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":[0.0022856,0.0004541254,0.003351499,0.001747864,0.0003118061,0.00130528,0.0002683712,0.005399758,0.655844,0.005807286,0.0132021,0.3100224],"study_design_scores_gemma":[0.0002875907,0.0004981363,0.005821043,0.0002436254,0.0003134749,0.002971247,0.0001687243,0.0817636,0.7821919,0.009552917,0.1158569,0.0003309183],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03537083,0.001114169,0.8994556,0.0001573951,0.0002120453,0.0004857967,0.009522194,0.05042697,0.003255003],"genre_scores_gemma":[0.06713459,0.0008663284,0.8845682,0.000170071,0.00008755056,0.0007156489,0.03942082,0.003403202,0.003633485],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007719444,"threshold_uncertainty_score":0.02582413,"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."}}