University of California, Irvine

About: University of California, Irvine is a education organization based out in Irvine, California, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 47031 authors who have published 113602 publications receiving 5521832 citations. The organization is also known as: UC Irvine & UCI.

The Value of Museum Collections for Research and Society

Abstract: Many museums and academic institutions maintain first-rate collections of biological materials, ranging from preserved whole organisms to DNA libraries and cell lines. These biological collections make innumerable contributions to science and society in areas as divergent as homeland secu- rity ,p ublic health and safety, monitoring of environmental change, and traditional taxonomy and systematics. Moreover, these collections save governments and taxpayers many millions of dollars each year by effectively guiding government spending, preventing catastrophic events in public health and safety, eliminating redundancy, and securing natural and agricultural resources. However, these contributions are widely underappre- ciated by the public and by policymakers, resulting in insufficient financial support for maintenance and improvement of biological collections.

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 layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ.