{"id":"W2156824433","doi":"10.1109/iembs.2005.1615546","title":"Elimination of Redundant Protein Identifications in High Throughput Proteomics","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Redundancy (engineering); Proteomics; Computer science; Tandem mass spectrometry; Computational biology; Throughput; Identification (biology); Mass spectrometry; Set (abstract data type); Chemistry; Biology; Chromatography; Biochemistry; Gene","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":[0.0001031535,0.00009107077,0.0001182626,0.00005861997,0.00004692643,0.00001288893,0.0001898806,0.00008537374,0.0003007986],"category_scores_gemma":[0.00003470908,0.00009274505,0.00003538713,0.0001791699,0.00005408228,0.0001581127,0.00004365872,0.0001276178,0.00001740172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001037333,"about_ca_system_score_gemma":0.00003546289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001007137,"about_ca_topic_score_gemma":0.00007124261,"domain_scores_codex":[0.9991488,0.000005873168,0.0003841452,0.0002185678,0.0001099478,0.000132593],"domain_scores_gemma":[0.9993246,0.00001445066,0.0001633982,0.0003898382,0.00008174626,0.00002592565],"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":[0.000006958275,0.0001405567,0.00008992375,0.0000317509,0.000002659477,1.275382e-7,0.00006553398,0.0001321365,0.8574984,0.1382499,0.00005037335,0.00373167],"study_design_scores_gemma":[0.0001894494,0.000006510527,0.0002010807,0.00004132101,0.000003587836,0.000001337772,0.00005235546,0.001002612,0.9752206,0.02126645,0.001910664,0.0001040688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8014449,0.00002404116,0.1829165,0.002406297,0.000005731019,0.0007277964,0.00002768931,0.0001506733,0.01229635],"genre_scores_gemma":[0.6545895,0.00001667107,0.3424917,0.00001221801,0.00002902978,0.0006301103,0.00002812156,0.00001127837,0.002191387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1595752,"threshold_uncertainty_score":0.3782033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0119221859505688,"score_gpt":0.2775342445020165,"score_spread":0.2656120585514477,"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."}}