<p>Extracting more biologically relevant features from multiplexed imaging with a Multi-Encoder Variational AutoEncoder (ME-VAE)</p>

Luke Ternes; Mark Dane; Marilyne Labrie; Gordon Mills; Joe Gray; Laura Heiser; Young Hwan Chang

doi:10.7490/f1000research.1118845.1

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Ternes L, Dane M, Labrie M et al. Extracting more biologically relevant features from multiplexed imaging with a Multi-Encoder Variational AutoEncoder (ME-VAE) [version 1; not peer reviewed]. F1000Research 2021, 10:1159 (poster) (https://doi.org/10.7490/f1000research.1118845.1)

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Extracting more biologically relevant features from multiplexed imaging with a Multi-Encoder Variational AutoEncoder (ME-VAE)

Luke Ternes, Mark Dane, Marilyne Labrie, Gordon Mills, Joe Gray, Laura Heiser, Young Hwan Chang¹

Published 15 Nov 2021 (https://doi.org/10.7490/f1000research.1118845.1)

Author Affiliations

¹ Oregon Health and Science University, USA

Copyright © 2021 Ternes L et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 15 Nov 2021

Extracting more biologically relevant features from multiplexed imaging with a Multi-Encoder Variational AutoEncoder (ME-VAE)
[version 1; not peer reviewed]

Luke Ternes, Mark Dane, Marilyne Labrie, Gordon Mills, Joe Gray, Laura Heiser, Young Hwan Chang¹

Author Affiliations

¹ Oregon Health and Science University, USA

Presented at

Pacific Symposium on Biocomputing (PSB) 2021

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Variational Autoencoder, Computer Vision, Muliplexed Imaging, Single-Cell Analysis

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