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• W2950907595 abstract "Abstract Disease 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." @default.
• W2950907595 created "2019-06-27" @default.
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• W2950907595 date "2019-06-24" @default.
• W2950907595 modified "2023-10-05" @default.
• W2950907595 title "Deriving Disease Modules from the Compressed Transcriptional Space Embedded in a Deep Auto-encoder" @default.
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• W2950907595 doi "https://doi.org/10.1101/680983" @default.
• W2950907595 hasPublicationYear "2019" @default.
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