{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00195691,0.001041385,0.0007631879,0.0008419778,0.0004643883,0.001113359,0.001378884,0.001151195,0.002334812],"category_scores_gemma":[0.00513703,0.0003219499,0.001906577,0.0006029667,0.001139921,0.001104253,0.001564974,0.001946823,0.0005435515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007100953,"about_ca_system_score_gemma":0.0008469002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001282528,"about_ca_topic_score_gemma":0.001029639,"domain_scores_codex":[0.9992452,0.0003730857,0.00003685368,0.0001281658,0.0001812459,0.00003555148],"domain_scores_gemma":[0.9981248,0.001283776,0.0001911919,0.0001740149,0.000165698,0.00006049666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001304684,0.0001217778,0.004675444,0.0004321653,0.0002772649,0.0003545144,0.0002341629,0.781134,0.00864693,0.08824369,0.003202079,0.1125475],"study_design_scores_gemma":[0.000009104378,0.00002291566,0.0002142357,0.00001075701,0.00001046428,0.00003079093,0.00000617192,0.9743825,0.0008956409,0.02313298,0.001276176,0.000008307128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008774272,0.0001617133,0.9878215,0.0003927742,0.00003607858,0.0000468815,0.0001811856,0.001734827,0.0008507037],"genre_scores_gemma":[0.2469358,0.000445619,0.7492349,0.000278151,0.0001059702,0.000450281,0.0009049317,0.0005132637,0.001131014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002334812,"threshold_uncertainty_score":0.01034927,"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."}}