AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.

ABSTRACT: Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of

SUBMITTER: Ahammad I 

PROVIDER: S-EPMC11179120 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature