{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002494837,0.0004080971,0.0003361337,0.00007568592,0.0002600871,0.00007713241,0.0005678613,0.0002735563,0.0001671677],"category_scores_gemma":[0.00005252345,0.0004434183,0.000319737,0.00011676,0.00006395981,0.000086989,0.0001160521,0.0004709482,0.0000170795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000105253,"about_ca_system_score_gemma":0.00008518594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004809444,"about_ca_topic_score_gemma":0.000004501111,"domain_scores_codex":[0.9981635,0.00002211981,0.0003714733,0.0006727626,0.0002064172,0.0005637585],"domain_scores_gemma":[0.9985507,0.00003863627,0.000325983,0.0008223613,0.0001172097,0.0001451321],"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":[0.00004012301,0.00007526628,0.0004411033,0.0002542479,0.00003504481,0.00003160593,0.00009949572,0.0007312134,0.995665,0.001119839,0.00006064708,0.001446458],"study_design_scores_gemma":[0.0004836865,0.00003865105,0.00001351382,0.00007547256,0.00005413853,0.000007121133,0.00002278493,0.01234897,0.9787951,0.005773186,0.00187159,0.0005158606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1678255,0.00004550494,0.8279836,0.0002494002,0.0000400137,0.0009242335,0.000108942,0.0003273867,0.002495511],"genre_scores_gemma":[0.3858343,0.000003044013,0.6120087,0.0001997338,0.00009024048,0.001075176,0.0002454115,0.000130714,0.0004126324],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2180089,"threshold_uncertainty_score":0.9998018,"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."}}