Abstract
The development of high-throughput technology has provided a reliable technical guarantee for an increased amount of available data on biological networks. Network alignment is used to analyze these data to identify conserved functional network modules and understand evolutionary relationships across species. Thus, an efficient computational network aligner is needed for network alignment. In this paper, the classic bat algorithm is discretized and applied to the network alignment. The bat algorithm initializes the population randomly and then searches for the optimal solution iteratively. Based on the bat algorithm, the global pairwise alignment algorithm BatAlign is proposed. In BatAlign, the individual velocity and the position are represented by a discrete code. BatAlign uses a search algorithm based on objective function that uses the number of conserved edges as the objective function. The similarity between the networks is used to initialize the population. The experimental results showed that the algorithm was able to match proteins with high functional consistency and reach a relatively high topological quality.
Highlights
With the development of high-throughput technology, such as the yeast two-hybrid system [1], an increasing amount of biological data are being modeled into biological networks
The number of iterations in BatAlign was set to 1000; the size of the population was set to 40; N = 10; that is, when the optimal solution is not updated after 10 times, the current optimal solution was output as the final alignment result
In order to ensure the fairness of the comparison, parameter α was set to 0:4 in all the algorithms that use alpha to control the weight of topological similarity and sequence score, and this value was recommended by ModuleAlign
Summary
With the development of high-throughput technology, such as the yeast two-hybrid system [1], an increasing amount of biological data are being modeled into biological networks. Network alignment is a more efficient method for analyzing biological networks, in comparison to biological experiments [6], and can be used to discover functional modules among networks [7] and predict the unknown function of proteins [8]. Homologous protein pairs of less-studied biological networks can be discovered by comparison with biological networks that have been more extensively studied, to detect potential functions of unknown proteins [9, 10]. The sequence similarity, which is an internal property of proteins [11, 12], between network nodes needs to be used in biological alignment. The majority of the PPI network alignment algorithms use a combination of sequence similarity and topological similarity [13,14,15]
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