In cancer research, datasets often encompass a vast number of genetic, proteomic, and clinical features. Elastic Net can efficiently handle such high-dimensional data, making it ideal for identifying biomarkers and understanding complex biological interactions. Its ability to perform both feature selection and regularization helps in developing more accurate predictive models for cancer prognosis and treatment responses.
