Improving the Quantification of DNA Sequences Using Evolutionary Information Based on Deep Learning.

ABSTRACT: It is known that over 98% of the human genome is non-coding, and 93% of disease associated variants are located in these regions. Therefore, understanding the function of these regions is important. However, this task is challenging as most of these regions are not well understood in terms of their functions. In this paper, we introduce a novel computational model based on deep neural networks, called DQDNN, for quantifying the function of non-coding DNA regions. This model combines convolution layers for capturing regularity motifs at multiple scales and recurrent layers for capturing long term dependencies between the captured motifs. In addition, we show that integrating evolutionary information with raw genomic sequences improves the performance of the predictor significantly. The prop

SUBMITTER: Tayara H 

PROVIDER: S-EPMC6952993 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature