High order structures (cavities and cliques) of the gene network of influenza
A virus reveal tight associations among viruses during evolution and are key
signals that indicate viral cross-species infection and cause pandemics. As
indicators for sensing the dynamic changes of viral genes, these higher order
structures have been the focus of attention in the field of virology. However,
the size of the viral gene network is usually huge, and searching these
structures in the networks introduces unacceptable delay. To mitigate this
issue, in this paper, we propose a simple-yet-effective model named HyperSearch
based on deep learning to search cavities in a computable complex network for
influenza virus genetics. Extensive experiments conducted on a public influenza
virus dataset demonstrate the effectiveness of HyperSearch over other advanced
deep-learning methods without any elaborated model crafting. Moreover,
HyperSearch can finish the search works in minutes while 0-1 programming takes
days. Since the proposed method is simple and easy to be transferred to other
complex networks, HyperSearch has the potential to facilitate the monitoring of
dynamic changes in viral genes and help humans keep up with the pace of virus
mutations.Comment: work in progres