Subdue: compression-based frequent pattern discovery in graph data
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Biological sequences encoding for supervised classification
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
A novel approach for mining representative spatial motifs of proteins
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Recently, the principles of graph theory are being adopted to address molecular and chemical structures investigations such as 3D protein structure prediction and spatial motifs discovery. Proteins have been parsed into graphs according to several approaches and methods and then studied based on graph theory concepts and data mining tools. In this paper we make a brief survey on the most used graph-based representations and we propose a naïve method to help with the protein graph making since a key step of a valuable protein structure mining process is to build concise and correct graphs holding reliable information. We, also, show that some existing and widespread methods present remarkable weaknesses and don't really reflect the real protein conformation.