{"id":"W3188940523","doi":"10.1021/acs.analchem.1c01204","title":"Analyzing Assay Specificity in Metabolomics Using Unique Ion Signature Simulations","year":2021,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Metabolomics; Mass spectrometry; Selected reaction monitoring; Resolution (logic); Fragment (logic); Proteomics; Computational biology; Chromatography; Biological system; Tandem mass spectrometry; Computer science; Algorithm; Artificial intelligence; Biochemistry","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":[0.001265755,0.0008261659,0.0006165172,0.0004757373,0.0004746277,0.0006834597,0.0009922544,0.001117625,0.001858918],"category_scores_gemma":[0.004029583,0.000325241,0.0006878594,0.0004265347,0.0005598742,0.0008379251,0.0008635075,0.0008351647,0.0002723407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00101167,"about_ca_system_score_gemma":0.001252963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005678406,"about_ca_topic_score_gemma":0.002776743,"domain_scores_codex":[0.9996746,0.00009925127,0.00001619891,0.00005897489,0.0001013726,0.00004960209],"domain_scores_gemma":[0.998181,0.001351559,0.0001393106,0.00009953243,0.0001603919,0.00006823992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001241863,0.00007052919,0.002039226,0.000124912,0.00004406845,0.00008508158,0.00003706603,0.975577,0.009871162,0.007463729,0.0002845782,0.004278454],"study_design_scores_gemma":[0.00001093479,0.00002540017,0.000182508,0.000004139648,0.000006328641,0.00001019811,0.000005027193,0.9958124,0.002623139,0.001018974,0.0002960502,0.000004859321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6251631,0.00106794,0.3548122,0.0006635542,0.0001111137,0.0002647227,0.001240524,0.0009840284,0.01569273],"genre_scores_gemma":[0.9406151,0.0004408134,0.05632552,0.0001850007,0.00001509625,0.0003019565,0.0004728081,0.0001168522,0.00152676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005678406,"threshold_uncertainty_score":0.01129073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01908997167624191,"score_gpt":0.2837188227556717,"score_spread":0.2646288510794298,"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."}}