{"id":"W2124312647","doi":"10.1093/bioinformatics/bth186","title":"Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry","year":2004,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Kernel (algebra); Tandem mass spectrometry; Identification (biology); Software; Fragment (logic); Correlative; Mass spectrometry; Artificial intelligence; Data mining; Algorithm; Chemistry; Mathematics; Operating system","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":[0.001505421,0.0006432781,0.0007175102,0.001075536,0.0002781585,0.0005457469,0.0009694951,0.0004793023,0.001291209],"category_scores_gemma":[0.0037109,0.0002717009,0.0003708825,0.001264029,0.0005316011,0.001327859,0.001215714,0.0007175811,0.001100904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002826726,"about_ca_system_score_gemma":0.0007056776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004526616,"about_ca_topic_score_gemma":0.0005458744,"domain_scores_codex":[0.9993723,0.0001806019,0.00004492481,0.0001070072,0.0002636573,0.00003154155],"domain_scores_gemma":[0.9982163,0.0007847203,0.0003023228,0.0003604916,0.0002559772,0.00008007425],"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":[0.0007188689,0.0003834045,0.008282826,0.0005010121,0.000196718,0.0007583198,0.0001186242,0.1067597,0.2007378,0.01276806,0.0044157,0.664359],"study_design_scores_gemma":[0.00003877002,0.00009822685,0.001719704,0.000003777112,0.00002391807,0.0004970969,0.00001152652,0.9458808,0.04290476,0.007713146,0.001080921,0.00002746995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04343497,0.000109926,0.9542894,0.00005385917,0.00001132898,0.00002831763,0.00006535588,0.001759468,0.0002472423],"genre_scores_gemma":[0.2878007,0.0001764911,0.7106351,0.00004106012,0.00002395048,0.00006625065,0.0003423076,0.0001474242,0.0007668067],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001505421,"threshold_uncertainty_score":0.007961512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01677280231483643,"score_gpt":0.2691784092850094,"score_spread":0.252405606970173,"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."}}