{"id":"W2009481149","doi":"10.3182/20070604-3-mx-2914.00009","title":"FEATURE SELECTION AND CLASSIFICATION OF METABOLOMIC DATA USING SUPPORT VECTOR MACHINES","year":2007,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Feature selection; Computer science; Support vector machine; Data mining; Machine learning; Terminology; Artificial intelligence; Exploratory data analysis; Selection (genetic algorithm); Task (project management); Feature (linguistics); Biological data; Bioinformatics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004395972,0.0001398944,0.0001980582,0.00009266673,0.00007930196,0.00002274105,0.0001759211,0.0001218268,0.000006953505],"category_scores_gemma":[0.0001576381,0.0001284774,0.00003270725,0.0001752025,0.00006869687,0.00001825671,0.0001935863,0.00008250168,7.956529e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001024188,"about_ca_system_score_gemma":0.00003082094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001917245,"about_ca_topic_score_gemma":0.00003920469,"domain_scores_codex":[0.9991165,0.000005017655,0.0001757241,0.0003889342,0.0001131914,0.0002006578],"domain_scores_gemma":[0.9994614,0.000006396,0.0001775272,0.0001449446,0.0001606424,0.00004906488],"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":[0.00006409994,0.0000222874,0.0450392,0.00003884779,0.00007980375,1.298072e-7,0.0000392004,2.723714e-7,0.9488786,0.0004347717,0.001790175,0.003612584],"study_design_scores_gemma":[0.0006154333,0.0002843341,0.4306325,0.00001450103,0.0002124363,0.00006925657,0.000410073,0.002959682,0.5148996,0.0001492764,0.04936913,0.0003838542],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958613,0.002155277,0.001041333,0.0001968185,0.0001143304,0.0001367775,0.00004034152,0.00001425249,0.0004395594],"genre_scores_gemma":[0.9881693,0.0005420269,0.0105395,0.00003050936,0.0002408636,0.000002488512,0.00008619956,0.00001765718,0.0003714885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4339791,"threshold_uncertainty_score":0.5239158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02743268028186584,"score_gpt":0.2956803894032233,"score_spread":0.2682477091213574,"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."}}