Frequent Pattern Mining from Biological Sequences Based on Score Matrix
YUAN Ermao1, GUO Dan1, HU Xuegang1, WU Xindong1,2
1.School of Computer and Information, Hefei University of Technology, Hefei 230009. 2.Department of Computer Science, University of Vermont, Burlington, VT 05405
Abstract Mining significant frequent patterns from biological sequences is an important task in bioinformatics. An algorithm of mining approximate frequent pattern based on score matrix (MAPS) is proposed. Firstly, approximate matching score matrix (MSM) is constructed to handle insertion, replacement and deletion operations with interval constraints. Secondly, the approximate pattern matching based on score matrix (S-APM) scheme is designed to obtain counts of approximate occurrences of each pattern. Finally, a data driven pattern generation method and an Apriori-like rule are adopted to avoid unnecessary candidate patterns. Experiments on protein and DNA sequences show that the MAPS produces better performance, and it can be used to discover co-occurrence frequent patterns among different sequences.
Fund:Supported by National Natural Science Foundation of China-Joint Research Fund for Overseas Chinese, Hong Kong and Macao Young Scholars (No.61229301), Young Scientists Fund of National Natural Science Foundation of China (No.61305062)
About author:: (YUAN Ermao, born in 1991, master student. His research interests include pattern matching and data mining.) (GUO Dan(Corresponding author), born in 1983, Ph.D., associate professor. Her research interests include artificial intelligence and pattern mining.) (HU Xuegang, born in 1961, Ph.D., professor. His research interests include data mining and artificial intelligence.) (WU Xindong, born in 1963, Ph.D., professor. His research interests include data mining, system based on knowledge and world wide web information retrieval.)
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2016, Vol. 29 
