{"id":"W4412351439","doi":"10.1021/acs.jnatprod.4c01458","title":"Prediction of Bioactive Metabolites from American <i>Aconitum</i> Using Network Integrating Cellular Morphological Profiling and Mass Spectrometry Data","year":2025,"lang":"en","type":"article","venue":"Journal of Natural Products","topic":"Plant-based Medicinal Research","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Center for Complementary and Integrative Health; City University of New York","keywords":"Aconitum; Mass spectrometry; Profiling (computer programming); Metabolite profiling; Chemistry; Metabolome; Diterpene; Computational biology; Chromatography; Metabolomics; Biology; Stereochemistry; Computer science; Alkaloid","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":[0.0001326895,0.0004722314,0.000195007,0.001151683,0.0002538885,0.0004546757,0.0001525203,0.000234702,0.0004963363],"category_scores_gemma":[0.0002207791,0.0001310659,0.0004347304,0.0008864229,0.000129482,0.0002999567,0.0002694301,0.0001786959,0.0001857397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003049754,"about_ca_system_score_gemma":0.0004050734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004086998,"about_ca_topic_score_gemma":0.007539024,"domain_scores_codex":[0.9999168,0.000009874359,0.000004984781,0.00003715607,0.00001874087,0.00001240569],"domain_scores_gemma":[0.9998956,0.0000219813,0.00004051096,0.000005012477,0.00002024197,0.00001661959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008836428,0.0002458292,0.1860545,0.0005178899,0.000260874,0.0006889959,0.0001192184,0.02425642,0.7036626,0.000578346,0.0008512906,0.08188049],"study_design_scores_gemma":[0.0000427666,0.000748609,0.6199334,0.00004371512,0.0005388828,0.001059269,0.0003794457,0.2657557,0.1000006,0.001233611,0.01021217,0.00005191942],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809902,0.0006932574,0.01366222,0.00006796571,0.000006324883,0.0000713339,0.002424771,0.0002014474,0.001882416],"genre_scores_gemma":[0.9767853,0.0007849815,0.01554544,0.00003119853,0.000008133354,0.00005899074,0.005973739,0.00001554286,0.0007966865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004086998,"threshold_uncertainty_score":0.008126438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1612783022965524,"score_gpt":0.4340653420286293,"score_spread":0.272787039732077,"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."}}