Efficient Cavity Searching for Gene Network of Influenza A Virus
This work addresses a computational bottleneck in virology for monitoring viral gene dynamics, though it is incremental as it applies existing deep learning techniques to a specific domain problem.
The paper tackles the problem of efficiently searching high-order structures (cavities) in large gene networks of influenza A virus, which is crucial for tracking viral evolution and pandemic risks, and proposes HyperSearch, a deep learning model that reduces search time from days to minutes while outperforming other advanced methods.
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.