Machine learning–based design has gained traction in the sciences, most notably in the
design of small molecules, materials, and proteins, with societal applications ranging from …

One of the most fundamental problems in machine learning is finding interpretable
representations of the functions we learn. The Mobius transform is a useful tool for this …

With the rapid growth of large-scale machine learning models in genomics, Shapley values
have emerged as a popular method for model explanations due to their theoretical …

Protein language models leverage evolutionary information to perform state-of-the-art 3D
structure and zero-shot variant prediction. Yet, extracting and explaining all the mutational …

Using next-generation sequencing, it is now possible to screen up to billions of protein or
DNA sequences in parallel for a property of interest. Consequently, high-throughput …