文章基本信息

标题：Knowledge Discovery in Biochemical Pathways Using Minepathways
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作者：Gaol, Ford Lumban
期刊名称：Journal of Computer Science
印刷版ISSN：1549-3636
出版年度：2010
卷号：6
期号：11
页码：1276-1282
DOI：10.3844/jcssp.2010.1276.1282
出版社：Science Publications
摘要：Problem statement: The advancement of the biochemical research gives profound effect to the collection of biochemical data. Approach: In the recent years, data and networks in biochemical pathways are abundant that allow to do process mining in order to obtain useful information. By using graph theory as a tool to model these interactions, it can be formally find the solution. Results: The core of the problem of mining patterns is a subgraph isomorphism which until now has been in the NP-class problems. Early identification showed that in the context biochemical pathways has unique node labeling that result simplifying pattern mining problem radically. Conclusion: Process will be more efficient because the end result that is needed is maximum pattern that could reduce redundant patterns. The algorithm that used is a modification of the maximum item set patterns that are empirically most efficiently at this time.
关键词：Biochemical pathways; graph theory; subgraph isomorphism; NP problems; maximum itemset pattern