Adaptive Large Neighborhood Search Enhances Global Protein-Protein Network Alignment
Aligning protein-protein interaction networks from different species is a useful mechanism for figuring out orthologous proteins, predicting/verifying protein unknown functions or constructing evolutionary relationships. The network alignment problem is proved to be NP-hard, requiring exponential-ti...
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| Main Authors: | , , , , |
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| 格式: | Article |
| 語言: | English |
| 出版: |
H. : ĐHQGHN
2019
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| 主題: | |
| 在線閱讀: | http://repository.vnu.edu.vn/handle/VNU_123/64780 |
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| 總結: | Aligning protein-protein interaction networks from different species is a useful mechanism for figuring out orthologous proteins, predicting/verifying protein unknown functions or constructing evolutionary relationships. The network alignment problem is proved to be NP-hard, requiring exponential-time algorithms, which is not feasible for the fast growth of biological data. In this paper, we present a novel global protein-protein interaction network
alignment algorithm, which is enhanced with an extended large neighborhood search heuristics. Evaluated on benchmark datasets of yeast, fly, human and worm, the proposed algorithm outperforms state-of-the-art algorithms. Furthermore, the complexity of ours is polynomial, thus being scalable to large biological networks in practice |
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