Table 2.

Commonly faced challenges in computational biology and potential solution avenues when using DL.

Challenge	Experimental/non-DL solution	DL solution
Biased results	Improve study design	Identify forms and sources of technical bias
		Fair AI approaches
High infrastructure costs	Optimize code performance	Optimize DL architecture
	Parallelize code	Parallelize to low-cost devices
	Sub-sample analyzed data	Condense training data (e.g. coresets)
Lack of explainability	Statistical analyses	Explainable post-hoc methods
Limited training data	Generate and label more data	Data augmentation (e.g. GANs)
Overfitting	Regularization	Dropout
		Early stopping
		Smaller models
		Additional training data
Poor performance on novel data	Expand databases	Use larger models
		Analyze generalization potential