Integrating Multi-Omics Data for Advanced Clustering Analysis
Combining diverse biological data types to uncover meaningful patterns through machine learning.
Introduction
The era of multi-omics data brings exciting opportunities for deeper insights into biological processes. In one of my course projects, I explored how to integrate diverse data types such as gene expression, DNA methylation, and miRNA expression to perform clustering analysis and uncover meaningful patterns.
The Process
Data Integration
Combined three distinct omics datasets — gene expression, DNA methylation, and miRNA expression — to create a comprehensive view of biological systems.
Dimensionality Reduction
Reduced the high-dimensional data into manageable representations using neural network models.
Clustering & Evaluation
Validated clustering results through survival analysis to assess biological significance and clinical relevance.
Takeaways
This project enhanced my understanding of multi-omics data and demonstrated the power of machine learning in uncovering hidden insights within complex biological datasets.