Identification of therapeutic targets for Alzheimer's Disease Treatment using bioinformatics and machine learning.

ABSTRACT: Alzheimer's disease (AD) is a complex neurodegenerative disorder that currently lacks effective treatment options. This study aimed to identify potential therapeutic targets for the treatment of AD using comprehensive bioinformatics methods and machine learning algorithms. By integrating differential gene expression analysis, weighted gene co-expression network analysis, Mfuzz clustering, single-cell RNA sequencing, and machine learning algorithms including LASSO regression, SVM-RFE, and random forest, five hub genes related to AD, including PLCB1, NDUFAB1, KRAS, ATP2A2, and CALM3 were identified. PLCB1, in particular, exhibited the highest diagnostic value in AD and showed significant correlation with Braak stages and neuronal expression. Furthermore, Noscapine, PX-316, and TAK-901 were s

SUBMITTER: Xie Z 

PROVIDER: S-EPMC11785788 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature