Detection and Prevention of Virus Infection

Abstract

The pathogenic mechanism of viral infection is a complex process involving viral mutation, viral integration, and various aspects of the interaction between the viral genome and the host. Moreover, the virus mutation will lead to the failure of related vaccines, leading to the increasing of vaccine development costs and difficulties in virus prevention. With the accumulation of various types of data, using bioinformatics methods to mine the potential viral characteristics of the pathogenic process can help virus detection and diagnosis, to take intervention measures to prevent disease development or develop effective antiviral therapies. In this chapter, we first outlined traditional approaches and emerging technologies of virus detection and prevention, and then summarized the latest developments in the bioinformatics methods application in different fields of virus researches. The emergence of artificial intelligence provides advanced analysis techniques for revealing key factors of virus infection and has been widely used in the virology community. In particular, we highlight machine learning and deep learning algorithms to identify factors/categories from complex multidimensional data and uncover novel patterns of virus or disease risk prediction.