{"id":"W4406329468","doi":"10.3390/metabo15010044","title":"A Comprehensive Machine Learning Approach for COVID-19 Target Discovery in the Small-Molecule Metabolome","year":2025,"lang":"en","type":"article","venue":"Metabolites","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Qatar National Library; Qatar University","keywords":"Metabolome; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Computer science; Computational biology; Metabolomics; Medicine; Bioinformatics; Biology; Virology; Pathology; Disease","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001414944,0.001417868,0.001119676,0.001776797,0.0005929586,0.0008909409,0.0009500404,0.0008551324,0.0009057205],"category_scores_gemma":[0.001984909,0.0003150282,0.001675135,0.001169085,0.0002624512,0.0007927472,0.001001818,0.001231676,0.0005536341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005929815,"about_ca_system_score_gemma":0.001201341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004400416,"about_ca_topic_score_gemma":0.006137312,"domain_scores_codex":[0.9995316,0.0001233854,0.00003156747,0.0001374627,0.0001177255,0.00005828325],"domain_scores_gemma":[0.9994417,0.0002126738,0.00006592286,0.00006229379,0.0001686848,0.00004868511],"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":[0.0002436983,0.0003588395,0.01216119,0.0002045544,0.0007567522,0.0002764063,0.00007983717,0.6067857,0.0172372,0.002315838,0.004242555,0.3553374],"study_design_scores_gemma":[0.000003662461,0.00005499587,0.000905486,0.000007680396,0.00005046936,0.00002669094,0.000009166555,0.9956037,0.00133356,0.001415798,0.0005787386,0.00001010629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07739173,0.002403242,0.9140865,0.0008346374,0.000105948,0.0001482775,0.001037589,0.001984595,0.002007441],"genre_scores_gemma":[0.7107811,0.001470759,0.2808952,0.0004643931,0.0002901265,0.0003121441,0.002978763,0.0001155859,0.002691926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004400416,"threshold_uncertainty_score":0.008749604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04176554656197885,"score_gpt":0.3257039694909128,"score_spread":0.283938422928934,"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."}}