How is High Dimensional Data Analyzed in Cancer Research?
To tackle the challenges of high dimensional data, researchers employ various machine learning algorithms and statistical methods. Techniques such as principal component analysis (PCA), clustering, and random forests are commonly used to reduce dimensionality, identify patterns, and classify different cancer types. Deep learning approaches are also gaining traction for their ability to handle large and complex datasets.
