<p>Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data</p>

Savvas Kinalis; Finn Nielsen; Ole Winther; Frederik Otzen Bagger

doi:10.7490/f1000research.1117484.1

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Kinalis S, Nielsen F, Winther O and Bagger FO. Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data [version 1; not peer reviewed]. F1000Research 2019, 8(ISCB Comm J):1617 (poster) (https://doi.org/10.7490/f1000research.1117484.1)

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Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data

Savvas Kinalis¹, Finn Nielsen, Ole Winther, Frederik Otzen Bagger

Published 10 Sep 2019 (https://doi.org/10.7490/f1000research.1117484.1)

Author Affiliations

¹ Copenhagen University Hospital, Denmark

Copyright © 2019 Kinalis S 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 10 Sep 2019

Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data
[version 1; not peer reviewed]

Savvas Kinalis¹, Finn Nielsen, Ole Winther, Frederik Otzen Bagger

Author Affiliations

¹ Copenhagen University Hospital, Denmark

Presented at

ISMB/ECCB 2019

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Interpretable machine learning, deep learning, neural networks, manifold learning, expression profiles, single-cell RNA-sequencing, gene set enrichment analysis, functional analysis, biological pathway analysis

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