Why is Dimensionality Reduction Important in Cancer Research?
Cancer research often involves analyzing high-dimensional data from technologies like next-generation sequencing and mass spectrometry. Handling such large datasets can be computationally intensive and can lead to issues like the "curse of dimensionality," where the performance of algorithms deteriorates as the number of dimensions increases. Dimensionality reduction helps in simplifying these datasets, making it easier to identify meaningful patterns and correlations.
