Cancer research often involves high-throughput data, such as gene expression profiles, where the number of genes vastly exceeds the number of samples. Most of these genes have low or zero expression levels in any given sample, resulting in a sparse dataset. Using sparse matrices can:
1. Optimize Storage: Reduce memory usage by only storing non-zero elements. 2. Speed Up Computations: Enhance computational efficiency by focusing operations on non-zero elements. 3. Improve Scalability: Enable handling of larger datasets that would be impractical with dense matrices.
