Several methods are employed to address sparsity in cancer research:
Lasso Regression: Adds a penalty for the number of variables selected, promoting sparsity in the model. Principal Component Analysis (PCA): Reduces the dimensionality of the data, focusing on the most informative features. Sparse Matrix Techniques: Specialized algorithms that efficiently handle sparse data structures. Imputation: Fills in missing or zero values based on statistical methods or machine learning models.
