What are the Challenges in Computational Cancer Biology?
Despite its potential, computational cancer biology faces several challenges:
1. Data Complexity and Heterogeneity: Cancer is a highly heterogeneous disease, and the data generated from cancer studies are complex and multidimensional. Integrating and interpreting these diverse datasets require sophisticated computational approaches.
2. Model Accuracy: Developing accurate predictive models is challenging due to the variability in biological systems and the need for large, high-quality datasets. Ensuring that models are robust and generalizable is crucial for their successful application in clinical settings.
3. Computational Resources: Analyzing large datasets and running complex simulations require significant computational power and resources. Access to high-performance computing infrastructure and expertise in computational methods is essential.
