Untargeted urine metabolomics and machine learning provide potential metabolic signatures in children with autism spectrum disorder.

ABSTRACT:

Background

Complementary to traditional biostatistics, the integration of untargeted urine metabolomic profiling with Machine Learning (ML) has the potential to unveil metabolic profiles crucial for understanding diseases. However, the application of this approach in autism remains underexplored. Our objective was to delve into the metabolic profiles of autism utilizing a comprehensive untargeted metabolomics platform coupled with ML.

Methods

Untargeted metabolomics quantification (UHPLC/Q-TOF-MS) was performed for urine analysis. Feature selection was conducted using Lasso regression, and logistic regression, support vector machine, random forest, and extreme gradient boosting were utilized for significance stratification. Pathway enrichment analysis was performed to identi

SUBMITTER: Liu X 

PROVIDER: S-EPMC10912307 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature