{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008734614,0.00132371,0.001045486,0.001517624,0.0006784966,0.00205664,0.002038925,0.001300602,0.005996827],"category_scores_gemma":[0.001452949,0.0008368025,0.0005906071,0.0008201122,0.0008556857,0.001948938,0.002856916,0.002965219,0.004020876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005849381,"about_ca_system_score_gemma":0.0008010754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008194615,"about_ca_topic_score_gemma":0.002109378,"domain_scores_codex":[0.9990767,0.00009324468,0.00003029863,0.0002141561,0.0004712195,0.0001142863],"domain_scores_gemma":[0.9994513,0.0001477645,0.00006511,0.0001583848,0.0001138578,0.00006360488],"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":[0.001130434,0.0001892407,0.001965107,0.0004753418,0.0003104277,0.0006756043,0.0001594263,0.001341399,0.7945824,0.00863828,0.02389382,0.1666386],"study_design_scores_gemma":[0.00005957189,0.0001361148,0.0006271175,0.00002485589,0.00004463274,0.0005012973,0.00004774536,0.01728466,0.9516042,0.00262765,0.02696846,0.00007375662],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2533269,0.008354964,0.649727,0.004293491,0.001421028,0.0006780117,0.01134363,0.03924729,0.03160775],"genre_scores_gemma":[0.5217209,0.005404368,0.4226272,0.00238631,0.0001732456,0.0006600917,0.00832085,0.002989968,0.035717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005996827,"threshold_uncertainty_score":0.02006137,"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."}}