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dc.contributor.authorDwivedi, Sanjiv K.
dc.contributor.authorTjärnberg, Andreas
dc.contributor.authorTegner, Jesper
dc.contributor.authorGustafsson, Mika
dc.date.accessioned2020-02-13T10:23:07Z
dc.date.available2020-02-13T10:23:07Z
dc.date.issued2020-02-12
dc.date.submitted2019-07-11
dc.identifier.citationDwivedi, S. K., Tjärnberg, A., Tegnér, J., & Gustafsson, M. (2020). Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder. Nature Communications, 11(1). doi:10.1038/s41467-020-14666-6
dc.identifier.doi10.1038/s41467-020-14666-6
dc.identifier.urihttp://hdl.handle.net/10754/661511
dc.description.abstractDisease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein–protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes.
dc.description.sponsorshipThis work was supported by the Swedish foundation for strategic research, and Swedish Research Council. S.K.D. thanks to Andreas Kalin for helpful discussions and suggestions in deep learning, and Tejaswi VS Badam for his other helpful suggestions.
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttp://www.nature.com/articles/s41467-020-14666-6
dc.relation.urlhttps://www.nature.com/articles/s41467-020-14666-6.pdf
dc.rightsOpen Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleDeriving disease modules from the compressed transcriptional space embedded in a deep autoencoder
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.identifier.journalNature Communications
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionBioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
dc.contributor.institutionDepartment of Biology, Center For Genomics and Systems Biology, New York University, New York, NY 10008, USA.
dc.contributor.institutionCenter for Developmental Genetics, Department of Biology, New York University, New York, NY, USA.
dc.contributor.institutionUnit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
dc.contributor.institutionScience for Life Laboratory, Solna, Sweden.
kaust.personTegner, Jesper
dc.date.accepted2020-01-22
refterms.dateFOA2020-02-13T10:24:13Z


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Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Except where otherwise noted, this item's license is described as Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.