Evan Seitz

Quantitative biologist, currently working to advance deep learning for genomics as a Computational Postdoctoral Fellow @CSHL

[Peter Koo @pkoo562]

Which mutations rewire function of regulatory DNA?

Excited to share SEAM: Systematic Explanation of Attribtuion-based Mechanisms. SEAM is an explainable AI method that dissects cis-regulatory mechanisms learned by seq2fun genomic deep learning models.

Led by @EESetiz

1/N 🧵👇

[Peter Koo @pkoo562]

At @iclr_conf and interested in AI x Bio? Come see new work by the Koo Lab!

1. Oral presentation at @gembioworkshop by Evan Seitz (@EESeitz) on SEAM a method to decode the mechanistic impact of genetic variation on regulatory sequences with deep learning!
https://t.co/5xCTM9mp2t

[Peter Koo @pkoo562]

Very proud that SQUID is now published in Nature Machine Intelligence! @NatMachIntell

View only link (no subscription needed):
https://t.co/3HUDebmsLt

Full link (w/ subscription):
https://t.co/DXzE0HsIMi

@EESeitz @jbkinney @TheDMMcC

(Thanks to reviewers for feedback)

[Peter Koo @pkoo562]

In genomic deep learning, the trends right now are to build bigger models that consider longer sequence contexts. While predictions are more powerful, their scale makes them difficult to interpret. To address this gap, we have developed CREME.

Paper: https://t.co/ncQaFRjcC6

1/N

[Peter Koo @pkoo562]

Excited to share new work on "Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models” led by @EESeitz, jointly advised by me and @jbkinney and in collab with @TheDMMcC

Paper: https://t.co/dC8slzHIvr
Docs: https://t.co/R0AF6ZgdAr

[Evan Seitz @EESeitz]

@crescalantelab Thanks Carlos!

[Evan Seitz @EESeitz]

Thesis published: Analysis of Conformational Continuum and Free-energy Landscapes from Manifold Embedding of Single-particle Cryo-EM Ensembles of Biomolecules https://t.co/1Tc1rb4YpQ

[Evan Seitz @EESeitz]

@jeebu9 @adesgeorges Thank you, Jeevan!

[Evan Seitz @EESeitz]

@cryoET Thanks Ed!!

[Evan Seitz @EESeitz]

@deswastik @FrankLabCUMC Thank you Swastik!

[Evan Seitz @EESeitz]

I'm happy to announce the release of the ManifoldEM Python (beta) software suite, available at https://t.co/kS0rzSsbhj, featuring many interactive tools to help researchers explore highly heterogeneous cryo-EM data sets. #CryoEM #ManifoldEM

[Evan Seitz @EESeitz]

We've released a new paper exploring manifold analysis of cryo-EM data: https://t.co/g9A9NUgvch

[Evan Seitz @EESeitz]

@bj_charles @UCSFChimeraX @ZhongingAlong @joachimfrank This is an interesting idea to create plugins to work in ChimeraX. As for #ManifoldEM, we are currently working to make public a comprehensive user interface, which includes visualization of intermediate and final outputs, hopefully later this year. @bj_charles @joachimfrank

[Evan Seitz @EESeitz]

New paper -- A simulation of cryo-EM images from a continuously varying structure:
https://t.co/nmWf3KxGbA

[Evan Seitz @EESeitz]

Read about our latest #research on #cryoEM #Biology #protein, published with @SpringerNature in @naturemethods. Read here: https://t.co/a32WtXhTRJ

[Evan Seitz @EESeitz]

I just uploaded “Introduction to Electric Fields” to #Vimeo: https://t.co/6FOKNXDjnQ