{"id":"W2409556733","doi":"10.1021/acs.analchem.5b03126","title":"MyCompoundID MS/MS Search: Metabolite Identification Using a Library of Predicted Fragment-Ion-Spectra of 383,830 Possible Human Metabolites","year":2015,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates - Health Solutions; Alberta Innovates - Technology Futures; Alberta Innovates; Ministerul Cercetării, Inovării şi Digitalizării; Genome Canada","keywords":"Metabolite; Metabolome; Chemistry; Metabolomics; In silico; Computational biology; Identification (biology); Mass spectrometry; Chromatography; Biochemistry; Biology","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":[0.001081077,0.0009746728,0.0008744015,0.004035762,0.0006399369,0.0008530164,0.0009990504,0.0006002267,0.009052005],"category_scores_gemma":[0.001664796,0.0003672013,0.0006863235,0.002290083,0.0001952287,0.0007277958,0.0009119935,0.0003695281,0.003419066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000399175,"about_ca_system_score_gemma":0.0007206062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006890288,"about_ca_topic_score_gemma":0.001554161,"domain_scores_codex":[0.9995525,0.00005868589,0.00003553191,0.0001737556,0.0001516111,0.00002797349],"domain_scores_gemma":[0.999295,0.0002939585,0.0001559874,0.00006688467,0.0001142718,0.0000739918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002996528,0.0003278932,0.02599081,0.001459357,0.0004171444,0.002159131,0.0001989351,0.003279293,0.6707205,0.001185195,0.009891664,0.2813736],"study_design_scores_gemma":[0.0006927535,0.001927951,0.05359053,0.0001462147,0.0006740072,0.01217341,0.0001917606,0.07068995,0.7950648,0.002823513,0.06175731,0.0002677781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5262513,0.005668651,0.3351793,0.0008372699,0.00009942569,0.0008808305,0.07849742,0.03635149,0.01623428],"genre_scores_gemma":[0.3264165,0.00113693,0.6104707,0.0004881187,0.00008285922,0.0004872576,0.05745041,0.0009470472,0.002520268],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.009052005,"threshold_uncertainty_score":0.03028196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03558539018579833,"score_gpt":0.2933924032636898,"score_spread":0.2578070130778914,"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."}}