{"id":"W4403184708","doi":"10.1101/2024.10.04.616718","title":"Insights into Heart Failure Metabolite Markers through Explainable Machine Learning","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Montreal Heart Institute","funders":"","keywords":"Metabolite; Artificial intelligence; Logistic regression; Linear discriminant analysis; Machine learning; Metabolomics; Computer science; Support vector machine; Computational biology; Bioinformatics; Biology; Internal medicine; Medicine","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":[0.003147864,0.0007385623,0.0005620256,0.0009050019,0.0001874545,0.0009364657,0.000401839,0.0004131164,0.000852077],"category_scores_gemma":[0.004821794,0.0002716644,0.0007613399,0.000487955,0.0004562961,0.0006848191,0.0007182607,0.000888503,0.0002170931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003344061,"about_ca_system_score_gemma":0.000466127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007137195,"about_ca_topic_score_gemma":0.0006391098,"domain_scores_codex":[0.9994153,0.000354664,0.00001939862,0.00009757144,0.00007614236,0.00003694417],"domain_scores_gemma":[0.9979266,0.001361098,0.0003520485,0.0002181458,0.000111314,0.00003073558],"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":[0.001053852,0.0004709332,0.2065675,0.0004050327,0.00129933,0.0004948897,0.0004345707,0.4661301,0.06270665,0.01669185,0.001615962,0.2421294],"study_design_scores_gemma":[0.00002491481,0.0002193592,0.03189674,0.0000240521,0.00008641016,0.0001068681,0.00004445825,0.9421262,0.005213763,0.01941841,0.0008112147,0.0000275469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.444536,0.001250135,0.5509657,0.001033614,0.00003553856,0.00005642099,0.0006817081,0.0006731754,0.0007676984],"genre_scores_gemma":[0.9595262,0.0002539191,0.03934022,0.00009384927,0.00003839028,0.0000358691,0.0003971439,0.00003509785,0.000279236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003147864,"threshold_uncertainty_score":0.01664776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007601379951062658,"score_gpt":0.2175847077578688,"score_spread":0.2099833278068061,"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."}}