Enformer: predicting gene expression from sequence with Žiga Avsec

In this episode, Jacob Schreiber interviews Žiga Avsec about

a recently released model, Enformer. Their discussion begins with life

differences between academia and industry, specifically about how research

is conducted in the two settings. Then, they discuss the Enformer model,

how it builds on previous work, and the potential that models like it have

for genomics research in the future. Finally, they have a high-level discussion

on the state of modern deep learning libraries and which ones they use in their

day-to-day developing.

Links:

Effective gene expression prediction from sequence by integrating long-range interactions (Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli & David R. Kelley )

DeepMind Blog Post (Žiga Avsec)

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