Title
Using hybrid hierarchical K-means (HHK) clustering algorithm for protein sequence motif super-rule-tree (SRT) structure construction.
Abstract
Many algorithms or techniques to discover motifs require a predefined fixed window size in advance. Because of the fixed size, these approaches often deliver a number of similar motifs simply shifted by some bases or including mismatches. To confront the mismatched motifs problem, we use the super-rule concept to construct a Super-Rule-Tree (SRT) by a modified Hybrid Hierarchical K-means (HHK) clustering algorithm, which requires no parameter set-up to identify the similarities and dissimilarities between the motifs. By analysing the motif results generated by our approach, they are significant not only in sequence area but also in secondary structure similarity.
Year
DOI
Venue
2010
10.1504/IJDMB.2010.033523
IJDMB
Keywords
DocType
Volume
structure construction,clustering algorithm,fixed size,predefined fixed window size,similar motif,motif result,parameter set-up,protein sequence motif super-rule-tree,sequence area,modified hybrid hierarchical k-means,secondary structure similarity,mismatched motifs problem
Journal
4
Issue
ISSN
Citations 
3
1748-5673
5
PageRank 
References 
Authors
0.47
10
4
Name
Order
Citations
PageRank
Bernard Chen111415.75
Jieyue He212818.92
Stephen Pellicer3252.92
Yi Pan42507203.23