High-dimensional data is a common challenge in cancer research due to the large number of genomic and proteomic features. Techniques like Principal Component Analysis (PCA), t-SNE (t-distributed Stochastic Neighbor Embedding), and Autoencoders can be used for dimensionality reduction. These methods transform the original features into a lower-dimensional space while preserving as much variance as possible, facilitating more efficient and effective feature selection.
