Evan Seitz
@EESeitz
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Quantitative biologist, currently working to advance deep learning for genomics as a Computational Postdoctoral Fellow @CSHL
New York City
Joined July 2017
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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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
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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)
nature.com
Nature Machine Intelligence - The intersection of genomics and deep learning shows promise for real impact on healthcare and biological research, but the lack of interpretability in terms of...
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
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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
biorxiv.org
The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN...
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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
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Thesis published: Analysis of Conformational Continuum and Free-energy Landscapes from Manifold Embedding of Single-particle Cryo-EM Ensembles of Biomolecules
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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
github.com
ManifoldEM Python suite. Contribute to evanseitz/ManifoldEM_Python development by creating an account on GitHub.
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We've released a new paper exploring manifold analysis of cryo-EM data:
biorxiv.org
This work is based on the manifold-embedding approach to the study of biological molecules exhibiting conformational changes in a continuum. Previous studies established a workflow capable of...
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New paper -- A simulation of cryo-EM images from a continuously varying structure: https://t.co/nmWf3KxGbA
biorxiv.org
Molecular machines visit a continuum of conformational states as they go through work cycles required for their metabolic functions. Single-molecule cryo-EM of suitable in vitro systems affords the...
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Read about our latest #research on #cryoEM #Biology #protein, published with @SpringerNature in @naturemethods. Read here:
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