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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression
Marios G Krokidis1, Themis P Exarchos1, Panagiotis Vlamos1
1Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Greece.
Mathematical Biosciences and Engineering : MBE
|March 24, 2021
Summary
This review explores high-throughput omics methods for identifying biomarkers in neurodegenerative diseases like Alzheimer's and Parkinson's. It also discusses computational approaches for analyzing complex biological systems and treatment responses.
Area of Science:
- Biomedical Science
- Computational Biology
- Neuroscience
Background:
- Neurodegenerative diseases (ND), including Alzheimer's (AD) and Parkinson's (PD), are complex and heterogeneous pathologies.
- Understanding molecular dysfunctions is crucial for defining complex phenotypes in ND.
- High heterogeneity in ND presents challenges for early diagnosis and treatment monitoring.
Purpose of the Study:
- To review high-throughput omics methodologies for identifying biomarkers in AD and PD.
- To explore computational network-based approaches for modeling complex biological systems.
- To discuss data analysis pipelines for improving reproducibility in benchmarking studies.
Main Methods:
- Review of high-throughput omics methodologies (genomics, proteomics, metabolomics).
- Integration of computational network-based approaches.
- Incorporation of multilevel clinical information.
Main Results:
- Identification of potent biomarkers for AD and PD pathogenesis.
- Monitoring of dysfunctional molecular pathways and treatment responses.
- Discussion of principles for efficient data analysis pipelines.
Conclusions:
- High-throughput omics and computational methods are essential for understanding ND.
- Biomarker discovery and treatment response monitoring can be enhanced.
- Improved data analysis pipelines increase reproducibility in ND research.
