What are the Challenges in Using Algorithms in Bioanalytical Sciences?
The use of algorithms in bioanalytical sciences comes with several challenges:
Data Quality: Poor quality data can lead to inaccurate results, making data preprocessing a critical step. Complexity: Some biological systems are highly complex, requiring sophisticated algorithms that can handle multidimensional data. Interpretability: Understanding how an algorithm arrives at a conclusion can be difficult, especially with black-box models like deep learning. Computational Resources: High computational power and memory are often needed to process large datasets efficiently.
