{"id":"W3164474166","doi":"10.1186/s12859-021-04209-1","title":"SMILE: systems metabolomics using interpretable learning and evolution","year":2021,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Queen's University","funders":"Queen's University; National Research Council Canada; Compute Canada","keywords":"Interpretability; Metabolomics; Machine learning; Computer science; Artificial intelligence; Visualization; Mechanism (biology); Process (computing); Interface (matter); Disease; Data science; Big data; Bioinformatics; Data mining; Biology; Medicine","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":[0.0002320601,0.0001472144,0.0002239144,0.00005622491,0.0001580323,0.00008645453,0.00006889486,0.0001140526,0.000006333855],"category_scores_gemma":[0.000237632,0.0001398877,0.00006074059,0.0001392825,0.00005356697,0.00001237562,0.0002418593,0.0001050275,0.000004841479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000233328,"about_ca_system_score_gemma":0.0001080206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001281279,"about_ca_topic_score_gemma":0.00000896534,"domain_scores_codex":[0.9991431,0.00004978462,0.0003026055,0.0001668534,0.0001041392,0.0002334951],"domain_scores_gemma":[0.9994699,0.00001612598,0.0001373574,0.0001842625,0.0001295334,0.0000628144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001795378,0.0001973136,0.09928343,0.001819055,0.00119268,0.00001016778,0.0009988532,0.02665463,0.829747,0.03363775,0.002602219,0.003677404],"study_design_scores_gemma":[0.001353032,0.0002228248,0.003871027,0.00008913096,0.0002704839,0.0003616623,0.007946385,0.8564734,0.02914336,0.000193566,0.09924456,0.0008305338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4757864,0.01626253,0.5018755,0.0000135504,0.0006420428,0.0001851986,0.00002770237,0.00002922578,0.005177809],"genre_scores_gemma":[0.8727823,0.002709605,0.122628,0.00007184807,0.000212457,0.000009907246,0.0001006729,0.00002658332,0.001458625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8298188,"threshold_uncertainty_score":0.5704454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01321038801645317,"score_gpt":0.2477690208680376,"score_spread":0.2345586328515845,"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."}}