EVO: DNA Foundation Models - Eric Nguyen | Stanford MLSys #96

Episode 96 of the Stanford MLSys Seminar Series!
Sequence Modeling and Design from Molecular to Genome Scale with EVO
Speaker: Eric Nguyen
Bios:
Eric Nguyen is a PhD student at Stanford in the BioEngineering department. He's advised by Steve Baccus in neurobiology, Chris Ré in computer science, and Brian Hie in chemical engineering, and is a part of Hazy Research and Evo Design labs.
Abstract:
We report Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to genome scale. Using an architecture based on advances in deep signal processing, we scale Evo to 7 billion parameters with a context length of 131 kilobases (kb) at single-nucleotide, byte resolution. Trained on whole prokaryotic genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods. Advances in multi-modal and multi-scale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity.
--
Stanford MLSys Seminar hosts: Avanika Narayan, Benjamin Spector, Michael Zhang
Twitter:
/ avanika15​
/ bfspector
/ mzhangio
--
Check out our website for the schedule: mlsys.stanford.edu
Join our mailing list to get weekly updates: groups.google....
#machinelearning #ai #artificialintelligence #systems #mlsys #computerscience #stanford