#64 Enformer: predicting gene expression from sequence with Žiga Avsec скачать в хорошем качестве

#64 Enformer: predicting gene expression from sequence with Žiga Avsec

In this episode, Jacob Schreiber (https://jmschrei.github.io/) interviews Žiga Avsec (  / avsecz  ) about a recently released model, Enformer (https://www.nature.com/articles/s4159...) . 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 (https://www.nature.com/articles/s4159...) (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 (https://deepmind.com/blog/article/enf...) (Žiga Avsec) If you enjoyed this episode, please consider supporting the podcast on Patreon (  / bioinfochat  ) .