{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002398997,0.0001769004,0.0005456323,0.0002809328,0.000131544,0.00002124013,0.0004436071,0.0001355197,0.00002173339],"category_scores_gemma":[0.002867683,0.0001282592,0.00005149061,0.0008682303,0.0004606041,0.0002890166,0.0001962205,0.002223244,7.287362e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009988303,"about_ca_system_score_gemma":0.0003524082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000437735,"about_ca_topic_score_gemma":0.000001544973,"domain_scores_codex":[0.9977049,0.0006804375,0.0005983157,0.0003300456,0.0003303727,0.0003559314],"domain_scores_gemma":[0.9974276,0.001130564,0.000638367,0.0002340948,0.0004486827,0.0001206792],"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":[0.0009927638,0.000108537,0.02193368,0.0000620473,0.0004729869,0.000148681,0.00005207595,0.0001264406,0.9711652,0.00007046136,0.0008370219,0.004030109],"study_design_scores_gemma":[0.001141113,0.0003277711,0.006661136,0.0002036291,0.00066454,0.0001288705,0.0005773614,0.01511468,0.9728687,0.0006565021,0.001530347,0.000125326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752749,0.02077178,0.0008738624,0.001033009,0.001502067,0.0002469897,0.0001990285,0.00001242669,0.00008598436],"genre_scores_gemma":[0.9659185,0.000732817,0.03192069,0.0002736495,0.001046541,8.91827e-7,0.00006341608,0.000009022635,0.00003451262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03104683,"threshold_uncertainty_score":0.9659013,"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."}}