What are the Challenges of Sparsity in Clinical Applications?
In clinical settings, sparsity can complicate the development of predictive models and biomarker discovery. Sparse data may lead to high variance in model predictions, making it difficult to generalize findings. Additionally, the presence of many zero values can obscure important biological signals, necessitating advanced methods to extract meaningful insights.
