Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks
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RESEARCH
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Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks Yuanke Zhong1† , Jing Li2† , Junhao He1 , Yiqun Gao1 , Jie Liu1 , Jingru Wang1 , Xuequn Shang1 and Jialu Hu1,3* From The 18th Asia Pacific Bioinformatics Conference Seoul, Korea. 18-20 August 2020 *Correspondence: [email protected] † Yuanke Zhong and Jing Li contributed equally to this work. 1 School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China 3 Centre of Multidisciplinary Convergence Computing, School of Computer Science, Northwestern Polytechnical University, 1 Dong Xiang Road, 710129 Xi’an, China Full list of author information is available at the end of the article
Abstract Background: Network alignment is an efficient computational framework in the prediction of protein function and phylogenetic relationships in systems biology. However, most of existing alignment methods focus on aligning PPIs based on static network model, which are actually dynamic in real-world systems. The dynamic characteristic of PPI networks is essential for understanding the evolution and regulation mechanism at the molecular level and there is still much room to improve the alignment quality in dynamic networks. Results: In this paper, we proposed a novel alignment algorithm, Twadn, to align dynamic PPI networks based on a strategy of time warping. We compare Twadn with the existing dynamic network alignment algorithm DynaMAGNA++ and DynaWAVE and use area under the receiver operating characteristic curve and area under the precision-recall curve as evaluation indicators. The experimental results show that Twadn is superior to DynaMAGNA++ and DynaWAVE. In addition, we use protein interaction network of Drosophila to compare Twadn and the static network alignment algorithm NetCoffee2 and experimental results show that Twadn is able to capture timing information compared to NetCoffee2. Conclusions: Twadn is a versatile and efficient alignment tool that can be applied to dynamic network. Hopefully, its application can benefit the research community in the fields of molecular function and evolution. Keywords: PPI network, Dynamic network, Network alignment, Dynamic time warping
Background In recent years, due to the rapid development of biotechnology, we can obtain a large amount of biological data, such as: gene expression data, methylation data, protein interaction network data and so on [1]. Protein is a substance closely related to life and various forms of life activities. It plays a vital role in almost all life activities. Therefore, research
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