{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001092851,0.001906126,0.001543331,0.00132796,0.000318598,0.0009831795,0.002688856,0.0007166963,0.005162924],"category_scores_gemma":[0.002335436,0.0008422875,0.001043483,0.001928706,0.0004388108,0.001068556,0.0008172796,0.001837946,0.004980887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005045956,"about_ca_system_score_gemma":0.001092794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001816718,"about_ca_topic_score_gemma":0.001547102,"domain_scores_codex":[0.9996051,0.00004924347,0.00004567612,0.0000806428,0.0002004698,0.00001896937],"domain_scores_gemma":[0.9988592,0.0006586958,0.00006712996,0.0001165521,0.0002573907,0.00004105107],"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":[0.0001156265,0.00008220104,0.0004026353,0.001956253,0.0002484845,0.0001754015,0.00005633228,0.01358566,0.00907879,0.01035136,0.05632051,0.9076268],"study_design_scores_gemma":[0.0002937147,0.000114603,0.002313713,0.001195131,0.0003740606,0.00227132,0.0000585992,0.2643596,0.06040026,0.06485397,0.6035588,0.0002062706],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.002170808,0.01428196,0.9439424,0.0004465941,0.000320332,0.0001424775,0.001304169,0.0313831,0.006008119],"genre_scores_gemma":[0.012459,0.02556228,0.946057,0.0005368424,0.0001986133,0.0004961225,0.00499594,0.003060704,0.006633436],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005162924,"threshold_uncertainty_score":0.0172717,"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."}}