{"id":"W2001354860","doi":"10.1074/mcp.m110.005785","title":"Feature-matching Pattern-based Support Vector Machines for Robust Peptide Mass Fingerprinting","year":2011,"lang":"en","type":"article","venue":"Molecular & Cellular Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; La Trobe University","keywords":"Peptide mass fingerprinting; Computer science; Matching (statistics); Pattern recognition (psychology); Artificial intelligence; Pattern matching; Feature (linguistics); Data mining; Computational biology; Chemistry; Proteomics; Mathematics; Biology; Biochemistry; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002930789,0.001004793,0.00123444,0.001133126,0.0003324705,0.0007878251,0.001110921,0.0008816183,0.000841685],"category_scores_gemma":[0.008178266,0.0002874219,0.000796971,0.001152262,0.000375677,0.0009219161,0.0006962148,0.001132883,0.000496548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004608402,"about_ca_system_score_gemma":0.0008149011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001999312,"about_ca_topic_score_gemma":0.0008343749,"domain_scores_codex":[0.997543,0.0008214862,0.0003057139,0.0004870088,0.000640528,0.0002023405],"domain_scores_gemma":[0.997088,0.001512991,0.0003162037,0.000265846,0.000747118,0.00006982496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004428207,0.0001790861,0.002601875,0.0001793902,0.0001330105,0.0001073465,0.00007852262,0.31141,0.01854325,0.002239837,0.001302383,0.6627825],"study_design_scores_gemma":[0.00001009294,0.00007033927,0.0004238032,0.000005539295,0.000009025872,0.00002088762,0.0000116024,0.993557,0.004613712,0.001042049,0.0002259526,0.00000999294],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04230727,0.0003443973,0.9550608,0.00008148448,0.00003255301,0.00008059854,0.0001429543,0.001669629,0.0002802417],"genre_scores_gemma":[0.6048082,0.0001698719,0.3934442,0.00006903082,0.00003304985,0.0002780146,0.0005832724,0.00007702141,0.0005374154],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002930789,"threshold_uncertainty_score":0.01549971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746160999519444,"score_gpt":0.2366728086696383,"score_spread":0.2192111986744438,"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."}}