{"id":"W1975775976","doi":"10.1021/ac800954c","title":"Analysis of Metabolomic Data Using Support Vector Machines","year":2008,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":369,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Metabolomics; Support vector machine; Chromatography; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001966058,0.001038721,0.001018527,0.001718871,0.0002478285,0.0007961995,0.0004936431,0.0007093388,0.001448171],"category_scores_gemma":[0.006627497,0.0002118466,0.0006606118,0.001319217,0.0002453945,0.0008038979,0.0004213027,0.0007796056,0.0006065622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000253731,"about_ca_system_score_gemma":0.0003558398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001389047,"about_ca_topic_score_gemma":0.000780221,"domain_scores_codex":[0.9989415,0.0004171622,0.0001171202,0.0001846032,0.0002720339,0.00006761277],"domain_scores_gemma":[0.9969137,0.002298268,0.0002093449,0.000196392,0.0003320945,0.00005018287],"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":[0.0007293333,0.0003302399,0.01024444,0.000325809,0.0003791881,0.0002696535,0.000126989,0.2958236,0.02215022,0.002214815,0.002160136,0.6652457],"study_design_scores_gemma":[0.00001019674,0.0001104917,0.002394906,0.000007876693,0.00001299487,0.00005086392,0.00002411908,0.9913628,0.004111177,0.001418038,0.0004811952,0.00001541058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1471332,0.0007733985,0.8461773,0.000318482,0.00008474103,0.0001494179,0.0008440293,0.003719562,0.0007998287],"genre_scores_gemma":[0.6715631,0.0004121181,0.3257582,0.00005301346,0.00005823272,0.0002027386,0.001179857,0.000066021,0.0007066603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001966058,"threshold_uncertainty_score":0.01039761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05111351934831769,"score_gpt":0.3129713055520993,"score_spread":0.2618577862037816,"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."}}