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• W2022370714 startingPage "1276" @default.
• W2022370714 abstract "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." @default.
• W2022370714 created "2016-06-24" @default.
• W2022370714 creator A5080606292 @default.
• W2022370714 date "2010-11-01" @default.
• W2022370714 modified "2023-09-30" @default.
• W2022370714 title "Knowledge Discovery in Biochemical Pathways Using Minepathways" @default.
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• W2022370714 doi "https://doi.org/10.3844/jcssp.2010.1276.1282" @default.
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