@article {10.3844/jcssp.2010.1276.1282, article_type = {journal}, title = {Knowledge Discovery in Biochemical Pathways Using Minepathways}, author = {Gaol, Ford Lumban}, volume = {6}, number = {11}, year = {2010}, month = {Oct}, pages = {1276-1282}, doi = {10.3844/jcssp.2010.1276.1282}, url = {https://thescipub.com/abstract/jcssp.2010.1276.1282}, 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.}, journal = {Journal of Computer Science}, publisher = {Science Publications} }