A pilot study on machine learning approach to delineate metabolic signatures in intellectual disability.

ABSTRACT: Intellectual disability (ID) is a neurodevelopmental disorder characterized by cognitive delays. Inborn errors of metabolism constitute an important subgroup of ID for which various treatments options are available. We aimed to identify potential biomarkers of inherited metabolic disorders from the children with ID using tandem mass spectrometry and develop a novel machine learning algorithm to differentiate between the cases and the controls. All of the cases were having IQ score <70, gross motor delay, speech disorder and no recognizable symptoms of the condition. Metabolite profiling of ID individuals exhibited low tyrosine/large neutral amino acids, high citrulline/arginine ratios; elevated proline, alanine, phenylalanine, and ornithine, while a significant decrease in the level of ami

SUBMITTER: Nikam V 

PROVIDER: S-EPMC8115603 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature