{"id":"W4365450264","doi":"10.1038/s41592-023-01850-x","title":"BUDDY: molecular formula discovery via bottom-up MS/MS interrogation","year":2023,"lang":"en","type":"article","venue":"Nature Methods","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Government of Canada","keywords":"Interrogation; Annotation; Computer science; Metabolomics; False discovery rate; Tandem mass spectrometry; Computational biology; Ranking (information retrieval); Benchmarking; Data mining; Bioinformatics; Mass spectrometry; Information retrieval; Chemistry; Biology; Artificial intelligence; Chromatography; Biochemistry; Gene","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":[0.0009147601,0.000240835,0.0002805672,0.000162418,0.0001070076,0.00005246107,0.0002669017,0.0004703578,0.00002135503],"category_scores_gemma":[0.0003738781,0.0002085703,0.0002191218,0.0004875975,0.00005771694,0.00001107388,0.0003073053,0.0004405587,0.00001623669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002819368,"about_ca_system_score_gemma":0.00005011942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001621617,"about_ca_topic_score_gemma":0.00001883892,"domain_scores_codex":[0.9984682,0.0002230868,0.0002424565,0.0005010534,0.0001924643,0.0003727828],"domain_scores_gemma":[0.9991885,0.00005052196,0.0001070696,0.0004837492,0.00009810291,0.00007203053],"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":[0.00005141087,0.00002010363,0.0001505147,0.00002280403,0.0001625789,0.000006333747,0.00005287548,0.00001392754,0.9823073,0.003084358,0.003645454,0.01048236],"study_design_scores_gemma":[0.0004444922,0.0001424436,0.002005736,0.000009938154,0.00005670922,0.00001117745,0.00009542018,0.0002948425,0.8740577,0.004117789,0.118467,0.0002967621],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6543034,0.005870354,0.332013,0.000802124,0.002353045,0.000299985,0.00003692414,0.00009306629,0.004228132],"genre_scores_gemma":[0.9015623,0.0006887687,0.09046577,0.0008182502,0.0004990578,0.00005592581,0.0003574011,0.00005837878,0.005494159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2472589,"threshold_uncertainty_score":0.8505248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01117254604731763,"score_gpt":0.352265566298032,"score_spread":0.3410930202507144,"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."}}