In cancer research, random forests are employed for a variety of tasks such as gene expression analysis, predicting patient outcomes, and identifying biomarkers. The algorithm can handle large datasets with many variables, which is common in genomic studies. By analyzing patterns in the data, random forests can help identify which genes are most relevant to cancer prognosis and treatment.
