{"id":"W2024028567","doi":"10.1109/bibmw.2012.6470208","title":"A fuzzy cluster-based algorithm for peptide identification","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Institutes of Health; National Natural Science Foundation of China","keywords":"Silhouette; Fuzzy logic; Identification (biology); Computer science; Support vector machine; Matching (statistics); Artificial intelligence; Data mining; Pattern recognition (psychology); Algorithm; Mathematics; Biology","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":[0.003175641,0.001404899,0.00188763,0.003432328,0.002022465,0.001631925,0.003263721,0.002197501,0.004378154],"category_scores_gemma":[0.005627108,0.0006275899,0.001585449,0.002974286,0.001065742,0.00167926,0.00152739,0.001816957,0.002017486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001974165,"about_ca_system_score_gemma":0.002606911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01797868,"about_ca_topic_score_gemma":0.01338046,"domain_scores_codex":[0.9975327,0.0004634988,0.0001688656,0.0007201089,0.0009235751,0.0001911844],"domain_scores_gemma":[0.9982145,0.0005446011,0.0001254681,0.0001418462,0.0009153672,0.00005835941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002451635,0.00009043679,0.0006521715,0.0001921989,0.0001168365,0.00008204795,0.0002195994,0.26115,0.006714132,0.01628688,0.006822053,0.7074286],"study_design_scores_gemma":[0.00001912066,0.00003690052,0.0001698732,0.00001503463,0.00001449445,0.00004902961,0.0000277825,0.9879992,0.00262193,0.005876303,0.003140623,0.0000297201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001444315,0.0001003866,0.997248,0.00005227091,0.0000302462,0.00008688383,0.00004130756,0.0004401558,0.0005564799],"genre_scores_gemma":[0.02959023,0.00008545685,0.9682941,0.00006954249,0.00003143716,0.0002260384,0.0001487633,0.00008667753,0.001467747],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01797868,"threshold_uncertainty_score":0.03574806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01847048878896861,"score_gpt":0.2998742992442267,"score_spread":0.2814038104552581,"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."}}