A1. Research Questions and Aims
The primary research questions include:
– What multi-omics biomarkers can be identified that are associated with Alzheimer’s disease?
– How do these biomarkers correlate with disease progression and patient outcomes?
The aims are:
1. To integrate genomic, transcriptomic, proteomic, and metabolomic data to identify potential biomarkers.
2. To validate the identified biomarkers in independent cohorts.
A2. The Background and Scientific Rationale of the Proposed Research Project
Alzheimer’s disease (AD) is a complex neurodegenerative disorder characterized by cognitive decline and memory loss. Current diagnostic methods rely heavily on clinical assessments and neuroimaging, which may not capture the underlying biological complexities. Multi-omics approaches enable a comprehensive understanding of the molecular mechanisms involved in AD. By leveraging high-dimensional data from genomics, transcriptomics, proteomics, and metabolomics, this project aims to discover novel biomarkers that can facilitate early diagnosis, monitoring, and targeted therapies.
A3. A Brief Definition of the Methods to Be Used
This study will employ:
– Data Integration Techniques: Utilize bioinformatics tools to integrate various omics data.
– Statistical Analysis: Perform statistical tests to identify significant biomarkers.
– Machine Learning Algorithms: Apply machine learning methods for predictive modeling and validation of biomarkers.
A4. The Type and Size of Dataset Required
The project will require:
– Genomic, transcriptomic, proteomic, and metabolomic datasets from AD patients and healthy controls.
– A minimum of 500 samples across different stages of Alzheimer’s disease, including early-stage and advanced-stage patients.
A5. The Expected Value of the Research
The expected outcomes include the identification of reliable biomarkers that can improve early detection and monitoring of Alzheimer’s disease.
