Multi-Neighborhood Learning for Global Alignment in Biological Networks

Lijia Ma, Shiqiang Wang, Qiuzhen Lin, Jianqiang Li, Zhuhong You, Jiaxiang Huang, Maoguo Gong

科研成果: 期刊稿件文章同行评审

8 引用 (Scopus)

摘要

The global alignment of biological networks (GABN) aims to find an optimal alignment between proteins across species, such that both the biological structures and the topological structures of the proteins are maximally conserved. The research on GABN has attracted great attention due to its applications on species evolution, orthology detection and genetic analyses. Most of the existing methods for GABN are difficult to obtain a good tradeoff between the conservation of the biological structures and topological structures. In this paper, we propose a multi-neighborhood learning method for solving GABN (called as CLMNA). CLMNA first models GABN as an optimization of a weighted similarity which evaluates the conserved biological and topological similarities of an alignment, and then it combines a first-proximity, second-proximity and individual-aware proximity learning algorithm to solve the modeled problem. Finally, systematic experiments on 10 pairs of biological networks across 5 species show the superiority of CLMNA over the state-of-the-art network alignment algorithms. They also validate the effectiveness of CLMNA as a refinement method on improving the performance of the compared algorithms.

源语言英语
页(从-至)2598-2611
页数14
期刊IEEE/ACM Transactions on Computational Biology and Bioinformatics
18
6
DOI
出版状态已出版 - 2021
已对外发布

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