{"id":"W3151552372","doi":"10.3233/jpd-202476","title":"Lipidomics Prediction of Parkinson’s Disease Severity: A Machine-Learning Analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Parkinson s Disease","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Lipidome; Lipidomics; Parkinson's disease; Biomarker; Disease; Sphingolipid; Globotriaosylceramide; Internal medicine; Medicine; Biology; Bioinformatics; Biochemistry; Fabry disease","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002204579,0.0006307872,0.0006562067,0.001694669,0.0002885704,0.0006032427,0.000347443,0.0005964692,0.0007891599],"category_scores_gemma":[0.003076313,0.0001354664,0.001070712,0.0007681975,0.0002319742,0.000410663,0.000293473,0.0005845875,0.0003957749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003985617,"about_ca_system_score_gemma":0.0005787406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002686374,"about_ca_topic_score_gemma":0.001635854,"domain_scores_codex":[0.9995754,0.0001703518,0.00003136036,0.0001123637,0.0000625449,0.00004799926],"domain_scores_gemma":[0.9986307,0.0009439189,0.0001162899,0.00006958983,0.0001906173,0.00004887458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001323732,0.0007874419,0.3208554,0.000215487,0.0009351817,0.0003674023,0.0001124571,0.2243263,0.01748877,0.001012949,0.003415227,0.4291596],"study_design_scores_gemma":[0.00002523612,0.000170605,0.04891441,0.00002060393,0.00009242731,0.0001351947,0.00002208057,0.9468877,0.001605748,0.001720178,0.0003891745,0.00001668873],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7506949,0.002284608,0.2420855,0.0009146442,0.00006225336,0.0001534998,0.001358968,0.0008407735,0.001604953],"genre_scores_gemma":[0.9584421,0.0002759774,0.03979873,0.00006759835,0.00004633568,0.00005381465,0.0009792357,0.00001758501,0.0003186047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002686374,"threshold_uncertainty_score":0.01165909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01029674566318791,"score_gpt":0.2398967267372856,"score_spread":0.2295999810740977,"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."}}