Abstract
With the rapid developments of computer technology and biotechnology, a large amount of bioinformatics data such as bioprotein interaction networks have been generated. Protein interactions play an important role in most biochemical functions. In this paper, we propose an adaptive artificial immune system for the alignment of biological protein networks, which adaptively preserves excellent genes to maintain the diversity of individuals in population. Moreover, it adopts a crossover operator to simulate the sexual reproduction while it uses a simple mutation operator to mimic the mutations in the evolutionary process. Extensive experiments on real-world biological networks demonstrate the superiority of the proposed algorithm in maintaining the biological similarity and topological similarity of biological networks over the state-of-the-art algorithms.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant 61803269, in part by the Natural Science Foundation of Guangdong Province under Grant 2020A1515010790, and in part by the Technology Research Project of Shenzhen City under Grant by JCYJ20190808174801673.
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Wang, S., Ma, L., Zhang, X. (2020). Adaptive Artificial Immune System for Biological Network Alignment. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_49
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DOI: https://doi.org/10.1007/978-3-030-60802-6_49
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