Reservoir hosts prediction for COVID-19 by hybrid transfer learning model.

The recent outbreak of COVID-19 has infected millions of people around the world, which is leading to the global emergency. In the event of the virus outbreak, it is crucial to get the carriers of the virus timely and precisely, then the animal origins can be isolated for further infection. Traditional identifications rely on fields and laboratory researches that lag the responses to emerging epidemic prevention. With the development of machine learning, the efficiency of predicting the viral hosts has been demonstrated by recent researchers. However, the problems of the limited annotated virus data and imbalanced hosts information restrict these approaches to obtain a better result. To assure the high reliability of predicting the animal origins on COVID-19, we extend transfer learning and ensemble learning to present a hybrid transfer learning model. When predicting the hosts of newly discovered virus, our model provides a novel solution to utilize the related virus domain as auxiliary to help building a robust model for target virus domain. The simulation results on several UCI benchmarks and viral genome datasets demonstrate that our model outperforms the general classical methods under the condition of limited target training sets and class-imbalance problems. By setting the coronavirus as target domain and other related virus as source domain, the feasibility of our approach is evaluated. Finally, we show the animal reservoirs prediction of the COVID-19 for further analysing.

Investigating the Concept and Origin of Viruses.

The ongoing COVID-19 pandemic has piqued public interest in the properties, evolution, and emergence of viruses. Here, we discuss how these basic questions have surprisingly remained disputed despite being increasingly within the reach of scientific analysis. We review recent data-driven efforts that shed light into the origin and evolution of viruses and explain factors that resist the widespread acceptance of new views and insights. We propose a new definition of viruses that is not restricted to the presence or absence of any genetic or physical feature, detail a scenario for how viruses likely originated from ancient cells, and explain technical and conceptual biases that limit our understanding of virus evolution. We note that the philosophical aspects of virus evolution also impact the way we might prepare for future outbreaks.