Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data.

ABSTRACT:

Background

Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.

Methods

The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validat

SUBMITTER: Jo T 

PROVIDER: S-EPMC10579282 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature