NDLI: A novel approach for mining representative spatial motifs of proteins

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A novel approach for mining representative spatial motifs of proteins

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A novel approach for mining representative spatial motifs of proteins

Content Provider	ACM Digital Library
Author	Nguifo, Engelbert Mephu Saidi, Rabie Dhifli, Wajdi
Abstract	One of the used techniques to address protein structure investigation is to look for recurrent fragments (also called substructures or spatial motifs), then use these spatial motifs as patterns to characterize the proteins under consideration. An emergent trend consists on parsing proteins into graphs of amino acids. Hence, the search of recurrent spatial motifs is formulated as a process of frequent subgraph discovery where each subgraph represent a spatial motif. In this scope, several efficient approaches for frequent subgraph discovery have been proposed in literature. However, the set of discovered frequent subgraphs is too large to be efficiently analyzed and explored in any knowledge discovery process. In this paper, we propose a novel approach that shrinks the large size of the set of discovered subgraph-motifs by selecting the representative ones. Our method selects representative subgraph-motifs based on the evolutionary information of amino acids defined in the substitution matrices. The results issued from our experiments show that our approach decreases dramatically the number of motifs while enhancing their interestingness.
Starting Page	506
Ending Page	508
Page Count	3
File Format	PDF
ISBN	9781450316705
DOI	10.1145/2382936.2383001
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Frequent subgraphs Pattern selection Spatial motifs Protein structures Pattern summarization Graph mining
Content Type	Text
Resource Type	Article

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