Exploring Plausible Therapeutic Targets for Alzheimer's Disease using Multi-omics Approach, Machine Learning and...

Area of Science:

• Neuroscience
• Genomics
• Pharmacology

Background:

• Alzheimer's disease (AD) involves progressive neuronal deterioration, complicated by cellular heterogeneity.
• Identifying therapeutic targets is challenging as molecular changes' causality (cause vs. consequence) remains unclear.

Approach:

• Leverages single-cell RNA sequencing (scRNA-seq) to identify cell type-specific biomarkers.
• Analyzes multi-omics data (genomic, transcriptomic, epigenomic, proteomic) for disease insights.
• Explores machine learning for risk gene prioritization and drug candidate identification.

Key Points:

• scRNA-seq enables precise biomarker discovery across disease stages.
• Multi-omics integration provides a comprehensive view of AD pathogenesis.
• Machine learning accelerates the identification of AD-associated genes and natural product drug candidates.

Conclusions:

• This review highlights multi-omics and machine learning strategies for advancing Alzheimer's disease drug discovery.
• Identifying cell-specific targets and understanding molecular causality are crucial for developing effective AD therapeutics.