Prioritization of risk genes for Alzheimer's disease: an analysis framework using spatial and temporal gene expression data in the human brain based on support vector machine.

ABSTRACT: Background: Alzheimer's disease (AD) is a complex disorder, and its risk is influenced by multiple genetic and environmental factors. In this study, an AD risk gene prediction framework based on spatial and temporal features of gene expression data (STGE) was proposed. Methods: We proposed an AD risk gene prediction framework based on spatial and temporal features of gene expression data. The gene expression data of providers of different tissues and ages were used as model features. Human genes were classified as AD risk or non-risk sets based on information extracted from relevant databases. Support vector machine (SVM) models were constructed to capture the expression patterns of genes believed to contribute to the risk of AD. Results: The recursive feature eliminat

SUBMITTER: Wang S 

PROVIDER: S-EPMC10587557 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature