TY  - JOUR
AU  - Gaol, Ford Lumban 
PY  - 2010
TI  - Knowledge Discovery in Biochemical Pathways Using Minepathways
JF  - Journal of Computer Science
VL  - 6
IS  - 11
DO  - 10.3844/jcssp.2010.1276.1282
UR  - https://thescipub.com/abstract/jcssp.2010.1276.1282
AB  - 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.