Basset and Basenji with David Kelley

In this episode, Jacob Schreiber interviews David Kelley about

machine learning models that can yield insight into the consequences of

mutations on the genome. They begin their discussion by talking about

Calico Labs, and then delve into a series of papers that David has

written about using models, named Basset and Basenji, that connect genome

sequence to functional activity and so can be used to quantify the effect of

any mutation.

Links:

Calico Labs

Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks (David R. Kelley, Jasper Snoek, and John Rinn)

Sequential regulatory activity prediction across chromosomes with convolutional neural networks (David R. Kelley, Yakir A. Reshef, Maxwell Bileschi, David Belanger, Cory Y. McLean, and Jaspar Snoek)

Cross-species regulatory sequence activity prediction (David R. Kelley)

Basenji GitHub Repo

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