AIMC Topic: Amino Acid Metabolism, Inborn Errors

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Improving methylmalonic acidemia (MMA) screening and MMA genotype prediction using random forest classifier in two Chinese populations.

European journal of medical research Nov 10, 2024
BACKGROUND: Methylmalonic acidemia (MMA) is one of the most common hereditary organic acid metabolism disorders that endangers the lives and health of infants and children. Early detection and intervention before the appearance of a newborn's clinica...
Humans Machine Learning Female Infant, Newborn Male China Genotype East Asian People Random Forest Neonatal Screening Amino Acid Metabolism, Inborn Errors
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Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method.

World journal of pediatrics : WJP Feb 24, 2024
INTRODUCTION: Methylmalonic acidemia (MMA) is a disorder of autosomal recessive inheritance, with an estimated prevalence of 1:50,000. First-tier clinical diagnostic tests often return many false positives [five false positive (FP): one true positive...
Humans Machine Learning Female Infant, Newborn Sensitivity and Specificity Male Neonatal Screening Amino Acid Metabolism, Inborn Errors
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Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.

Journal of the American Medical Informatics Association : JAMIA Feb 16, 2024
OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa d...
Humans Machine Learning Natural Language Processing Dopamine Rare Diseases Amino Acid Metabolism, Inborn Errors Aromatic-L-Amino-Acid Decarboxylases
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