Prerequisites: One of CDS DS430/630, CDS DS436/636, ENG BE562, CDS DS526, or equivalent, or prior experience with computational biology. - Spring 2026 Topic: Learning From Large-Scale Biological Data We are living in the age of large-scale biological data. Over the last two decades, the cost of genetic sequencing has decreased faster than Moore's law, meaning that the abundance of data is outpacing the improvement in computational power to analyze it. So while these data are incredibly exciting, extracting biological meaning from the sheer quantity of heterogeneous data being generated requires cutting-edge computational methods. In this course we will study the modern algorithms and machine learning tools that have been developed to extract biological insights from these data including deep learning approaches such as for protein structure prediction and learning from sequence data, Bayesian approaches for gene function prediction and network construction, and graph-based approaches. We will examine these through a lens of common pitfalls in learning from biological data and how to best avoid them. By the end of this course students will understand both the contexts in which it is appropriate to use such tools as well as their limitations.

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