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

Joined July 2017
1 Photos and videos
Evan Seitz retweeted
9 Oct 2025
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 🧵👇
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Evan Seitz retweeted
26 Apr 2025
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! openreview.net/forum?id=PtjM…
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Evan Seitz retweeted
21 Jun 2024
Very proud that SQUID is now published in Nature Machine Intelligence! @NatMachIntell View only link (no subscription needed): rdcu.be/dLuZe Full link (w/ subscription): nature.com/articles/s42256-0… @EESeitz @jbkinney @TheDMMcC (Thanks to reviewers for feedback)

17 Nov 2023
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: biorxiv.org/content/10.1101/… Docs: squid-nn.readthedocs.io
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Evan Seitz retweeted
21 Mar 2024
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: biorxiv.org/content/10.1101/… 1/N
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Evan Seitz retweeted
17 Nov 2023
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: biorxiv.org/content/10.1101/… Docs: squid-nn.readthedocs.io
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2 Feb 2022
Thesis published: Analysis of Conformational Continuum and Free-energy Landscapes from Manifold Embedding of Single-particle Cryo-EM Ensembles of Biomolecules doi.org/10.7916/4n0v-wa24

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19 Oct 2021
I'm happy to announce the release of the ManifoldEM Python (beta) software suite, available at github.com/evanseitz/Manifol…, featuring many interactive tools to help researchers explore highly heterogeneous cryo-EM data sets. #CryoEM #ManifoldEM
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18 Sep 2019

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