{"id":"W2000034207","doi":"10.1016/j.drudis.2006.05.011","title":"Software for computational peptide identification from MS–MS data","year":2006,"lang":"en","type":"review","venue":"Drug Discovery Today","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Tandem mass spectrometry; Identification (biology); Peptide; Database search engine; Computational biology; Drug discovery; Mass spectrometry; Protein sequencing; Peptide mass fingerprinting; Proteomics; Chemistry; Computer science; Peptide sequence; Bioinformatics; Biology; Chromatography; Biochemistry; Search engine; Information retrieval","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001540557,0.000442534,0.0008273678,0.00006209167,0.0002151483,0.0002558588,0.001382653,0.000265055,0.0001201565],"category_scores_gemma":[0.00009726435,0.0004269429,0.0003114878,0.0001691024,0.0000732658,0.0005105241,0.0003964519,0.0003330668,0.00006475439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001835195,"about_ca_system_score_gemma":0.0002689153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001691621,"about_ca_topic_score_gemma":0.00003473615,"domain_scores_codex":[0.997517,0.00002198089,0.0008737829,0.001066774,0.0002492191,0.0002712099],"domain_scores_gemma":[0.9967259,0.0005564988,0.0007864423,0.001776058,0.00009607212,0.00005909823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001529523,0.0003614337,0.00002283122,0.0128276,0.0003210594,0.000003574477,0.00002970426,0.0007816147,0.00005150427,0.005209963,0.2166674,0.763708],"study_design_scores_gemma":[0.000118055,0.000001639267,0.000001613633,0.0018651,0.000368247,0.000002719096,0.000005516059,0.0002857934,0.0001028107,0.01665637,0.9801211,0.0004710031],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000008376874,0.5663218,0.3915617,0.000040966,0.0000740461,0.0007678553,0.0404559,0.0002192413,0.0005501082],"genre_scores_gemma":[0.0000102899,0.6750109,0.0753191,0.00002946521,0.0007083219,0.001541688,0.2383595,0.0001360695,0.008884718],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7634537,"threshold_uncertainty_score":0.9998183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04332395750182927,"score_gpt":0.3442307917079608,"score_spread":0.3009068342061315,"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."}}