{"id":"W2907470641","doi":"10.1186/s13321-018-0324-5","title":"BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification","year":2019,"lang":"en","type":"article","venue":"Journal of Cheminformatics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":513,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Eidgenössische Anstalt für Wasserversorgung Abwasserreinigung und Gewässerschutz; Genome Alberta; Agence Nationale de la Recherche; Alberta Innovates; Alberta Innovates - Health Solutions; Joint Programming Initiative A healthy diet for a healthy life; Canadian Institutes of Health Research; Genome Canada","keywords":"Computer science; Identification (biology); In silico; Computational biology; Metabolomics; Data mining; Machine learning; Bioinformatics; Chemistry; Biology; 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":[0.001606422,0.002500763,0.00195377,0.001639838,0.0005746701,0.001226184,0.002456405,0.001313485,0.008723734],"category_scores_gemma":[0.00358229,0.0009489363,0.002367864,0.001523554,0.0004669631,0.00122314,0.00163785,0.001604976,0.002644152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006487648,"about_ca_system_score_gemma":0.002886062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004137723,"about_ca_topic_score_gemma":0.006006012,"domain_scores_codex":[0.9994295,0.0001575742,0.00006475734,0.0001217844,0.0001847635,0.00004147925],"domain_scores_gemma":[0.9985477,0.0009679861,0.0001130647,0.0001218574,0.0001622965,0.00008706131],"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":[0.002510735,0.0007582209,0.01253211,0.005518966,0.002647784,0.00141874,0.0003247272,0.5893566,0.02857928,0.01763078,0.08972695,0.2489951],"study_design_scores_gemma":[0.0003353296,0.0001852359,0.000909964,0.00007010334,0.0001758337,0.0001897229,0.00001982639,0.9641703,0.007748459,0.005085112,0.02104705,0.00006309424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06152808,0.003532873,0.7336063,0.001124487,0.000280489,0.0006170503,0.03560005,0.1555223,0.008188278],"genre_scores_gemma":[0.215317,0.003451796,0.7176613,0.0007179357,0.0001375956,0.002151752,0.04843105,0.008599698,0.003531854],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008723734,"threshold_uncertainty_score":0.0291838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00830872921048072,"score_gpt":0.2322572317208913,"score_spread":0.2239485025104106,"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."}}