Journal of Computer Science | |
Knowledge Discovery in Biochemical Pathways Using Minepathways | Science Publications | |
Ford L. Gaol1  | |
关键词: Biochemical pathways; graph theory; subgraph isomorphism; NP problems; maximum itemset pattern; | |
DOI : 10.3844/jcssp.2010.1276.1282 | |
学科分类:计算机科学(综合) | |
来源: 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.
【 授权许可】
Unknown
【 预 览 】
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RO201911300989139ZK.pdf | 136KB | download |