What are the Methods for Multi-Omics Data Integration?
Several computational methods and frameworks have been developed to integrate multi-omics data:
Network-Based Approaches: These methods construct networks linking different omics layers and identify key nodes or pathways. Machine Learning: Algorithms such as random forests, support vector machines, and deep learning can predict disease outcomes based on integrated data. Dimensionality Reduction: Techniques like PCA and t-SNE simplify high-dimensional data for easier analysis and visualization. Bayesian Models: These statistical models estimate the probability distributions of different omics data and their interactions.
