DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.

ABSTRACT: Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent

SUBMITTER: Quang D 

PROVIDER: S-EPMC4914104 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature