Although the model has yet to be used in real-life applications, in research testing it has shown at least a 10 percent improvement in structure prediction accuracy compared to previous state-of-the-art methods according to Xinshi Chen, a Georgia Tech Ph.D. student specializing in machine learning and co-developer of the new tool. "The model uses an unrolled algorithm for solving a constrained optimization as a component in the neural network architecture, so that it can directly incorporate a solution constraint, or prior knowledge, to predict the RNA base-pairing matrix," said Chen. E2Efold is not only more accurate, it is also considerably faster than current techniques. Current methods are dynamic programming based, which is a much slower approach for predicting longer RNA sequences, such as the genomic RNA in a virus. E2Efold overcomes this drawback by using a gradient-based unrolled algorithm.

E2Efold is an end-to-end deep learning model developed at Georgia Tech that can predict RNA secondary structures, an important task used in virus analysis, drug design, and other public health applications. Although the model has yet to be used in real-life applications, in research testing it has shown at least a 10 percent improvement in structure prediction accuracy compared to previous state-of-the-art methods according to Xinshi Chen, a Georgia Tech Ph.D. student specializing in machine learning and co-developer of the new tool. "The model uses an unrolled algorithm for solving a constrained optimization as a component in the neural network architecture, so that it can directly incorporate a solution constraint, or prior knowledge, to predict the RNA base-pairing matrix," said Chen. E2Efold is not only more accurate, it is also considerably faster than current techniques. Current methods are dynamic programming based, which is a much slower approach for predicting longer RNA sequences, such as the genomic RNA in a virus.

As of February 7 at 13:00 UTC, China's National Health and Health Commission had received a total of 31,261 confirmed cases of the 2019 Novel Coronavirus (2019-nCov) outbreak and 26,359 suspected cases, which was a leap of 4,833 from the day before. As of February 8 at 03:00 UTC the 2019-nCoV had killed 725 people, all but one of them in China. Like SARS, HIV, Ebola, and influenza, the 2019-nCoV is an RNA virus. Its single-strand structure makes it more susceptible to mutation and more difficult to develop vaccines for. In mid-January, Chinese scientists isolated the first 2019-nCoV strain and published its genetic sequence to aid in independent detection of the virus.

Most emerging infectious diseases are caused by RNA viruses (1). Many of these that are newly found in humans have a natural mammal or bird reservoir; some are transmitted by arthropod vectors, such as mosquitos (2). If we do not know the reservoir host and/or vector, it is harder to identify individuals and populations at greatest risk of infection and to design an effective public health response. On page 577 of this issue, Babayan et al. (3) report their efforts to predict the reservoir hosts and vectors of human RNA viruses by applying machine-learning algorithms to virus genome sequence data.