Abstract
Because of the complexity of biological networks, motif mining is a key problem in data analysis for such networks. Researchers have investigated many algorithms aimed at improving the efficiency of motif mining. Here we propose a new algorithm for motif mining that is based on dynamic programming and backtracking. In our method, firstly, we enumerate all of the 3-vertex sub graphs by the method ESU, and then we enumerate sub graphs of other sizes using dynamic programming for reducing the search time. In addition, we have also improved the backtracking application in searching sub graphs, and the improved backtracking can help us search sub graphs more roundly. Comparisons with other algorithms demonstrate that our algorithm yields faster and more accurate detection of motifs.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Nos. 61425002, 61402066, 61402067, 31370778, 61370005, 31170797), the Basic Research Program of the Key Lab in Liaoning Province Educational Department (Nos. LZ2014049, LZ2015004), the Project Supported by Natural Science Foundation of Liaoning Province (No. 2014020132), the Project Supported by Scientific Research Fund of Liaoning Provincial Education (No. L2014499), and by the Program for Liaoning Key Lab of Intelligent Information Processing and Network Technology in University.
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Song, X., Zhou, C., Wang, B., Zhang, Q. (2015). A Method of Motif Mining Based on Backtracking and Dynamic Programming. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_30
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DOI: https://doi.org/10.1007/978-3-319-26181-2_30
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