Efficient Cavity Searching for Gene Network of Influenza A Virus

TL;DR

This paper introduces HyperSearch, a deep learning-based model that efficiently searches for cavities in large gene networks of influenza A virus, enabling rapid detection of structural changes relevant to viral evolution and pandemics.

Contribution

The paper presents HyperSearch, a novel deep learning approach that significantly reduces search time for cavities in viral gene networks compared to traditional methods.

Findings

HyperSearch outperforms other deep-learning methods in cavity detection.

HyperSearch completes searches in minutes, whereas 0-1 programming takes days.

The method is simple, effective, and transferable to other complex networks.

Abstract

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.