Technologies for quantifying biology have undergone significant advances in the past few decades, leading to datasets rapidly increasing in size and complexity. At the same time, deep learning models have gone from a curiosity to a massive field of research, with their advancements spilling over into other fields. Machine learning is not new to computational biology, as machine learning models have been used frequently in the field to account for the aforementioned size and complexity of the data. This dissertation asks whether the paradigm shift in machine learning that has led to the rise of deep learning models is causing a paradigm shift in computational biology. To answer this question, we begin with chapter 1, which gives background information helpful for understanding the main thesis chapters. We then move to chapter 2, which discusses standards necessary to ensure that research done with deep learning is reproducible. We continue to chapter 3, where we find that deep learning models may not be helpful in analyzing expression data. In chapters 4 and 5 we demonstrate that classical machine learning methods already allow scientists to uncover knowledge from large datasets. Then in chapter 6 we conclude by discussing the implications of the previous chapters and their potential future directions. Ultimately we find that while deep learning models are useful to various subfields of computational biology, they have yet to lead to a paradigm shift.