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dc.contributor.authorDwivedi, Sanjiv K.
dc.contributor.authorTjärnberg, Andreas
dc.contributor.authorTegner, Jesper
dc.contributor.authorGustafsson, Mika
dc.date.accessioned2019-12-01T13:06:28Z
dc.date.available2019-12-01T13:06:28Z
dc.date.issued2019-06-25
dc.identifier.citationDwivedi, S. K., Tjärnberg, A., Tegnér, J., & Gustafsson, M. (2019). Deriving Disease Modules from the Compressed Transcriptional Space Embedded in a Deep Auto-encoder. doi:10.1101/680983
dc.identifier.doi10.1101/680983
dc.identifier.urihttp://hdl.handle.net/10754/660336
dc.description.abstractDisease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, commonly used to 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 assuming the prior knowledge of a biological network. To this end we train a deep auto-encoder on a large transcriptional data-set. Our hypothesis is that such modules could be discovered in the deep representations within the auto-encoder when trained to capture the variance in the input-output map of the transcriptional profiles. Using a three-layer deep auto-encoder we find a statistically significant enrichment of GWAS relevant genes in the third layer, and to a successively lesser degree in the second and first layers respectively. In contrast, we found an opposite gradient where a modular protein-protein interaction signal was strongest in the first layer but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach, without assuming a particular biological network, is sufficient to discover groups of disease-related genes.
dc.publisherCold Spring Harbor Laboratory
dc.relation.urlhttp://biorxiv.org/lookup/doi/10.1101/680983
dc.relation.urlhttps://www.biorxiv.org/content/biorxiv/early/2019/06/24/680983.full.pdf
dc.rightsThe copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.titleDeriving Disease Modules from the Compressed Transcriptional Space Embedded in a Deep Auto-encoder
dc.typePreprint
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.eprint.versionPre-print
dc.contributor.institutionBioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
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
refterms.dateFOA2019-12-01T13:06:48Z


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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Except where otherwise noted, this item's license is described as The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.