{"id":"W3138869434","doi":"10.1101/2021.03.18.435788","title":"BATL: Bayesian annotations for targeted lipidomics","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lipidomics; Electrospray ionization; False discovery rate; Naive Bayes classifier; Computer science; Classifier (UML); Mass spectrometry; Bayes' theorem; Bayesian probability; Tandem mass spectrometry; Computational biology; Chromatography; Pattern recognition (psychology); Data mining; Bioinformatics; Chemistry; Artificial intelligence; Biology; Support vector machine; 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":[0.009846793,0.002238825,0.001356021,0.004258312,0.00145924,0.003265219,0.003356735,0.002489754,0.0199356],"category_scores_gemma":[0.02241012,0.001548271,0.001924162,0.001921132,0.001133015,0.003726719,0.003378741,0.002727995,0.01947921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002013068,"about_ca_system_score_gemma":0.003831886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005580989,"about_ca_topic_score_gemma":0.006431871,"domain_scores_codex":[0.9943257,0.001883068,0.0004223478,0.001065735,0.001968435,0.0003346271],"domain_scores_gemma":[0.9910828,0.004586097,0.0008277418,0.0009799299,0.002076627,0.0004467383],"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":[0.002769295,0.0004777711,0.0158226,0.003523994,0.0005124254,0.0008069306,0.00101486,0.09017983,0.05849194,0.05608092,0.2737881,0.4965313],"study_design_scores_gemma":[0.0001940849,0.0001022278,0.002064534,0.0003902981,0.00009342093,0.0004143256,0.0001227674,0.8082951,0.02528407,0.08129231,0.08157577,0.0001710443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004080491,0.00025845,0.9084747,0.000356195,0.0001105577,0.0001588749,0.01183244,0.07288048,0.001847844],"genre_scores_gemma":[0.05763299,0.0003311601,0.8942822,0.0005137103,0.0001315918,0.0007428399,0.03236451,0.01030407,0.003696893],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0199356,"threshold_uncertainty_score":0.06669128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045220887699878,"score_gpt":0.2278011662602672,"score_spread":0.2173489573832685,"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."}}