Title
Mining, compressing and classifying with extensible motifs.
Abstract
Motif patterns of maximal saturation emerged originally in contexts of pattern discovery in biomolecular sequences and have recently proven a valuable notion also in the design of data compression schemes. Informally, a motif is a string of intermittently solid and wild characters that recurs more or less frequently in an input sequence or family of sequences. Motif discovery techniques and tools tend to be computationally imposing, however, special classes of "rigid" motifs have been identified of which the discovery is affordable in low polynomial time.In the present work, "extensible" motifs are considered such that each sequence of gaps comes endowed with some elasticity, whereby the same pattern may be stretched to fit segments of the source that match all the solid characters but are otherwise of different lengths. A few applications of this notion are then described. In applications of data compression by textual substitution, extensible motifs are seen to bring savings on the size of the codebook, and hence to improve compression. In germane contexts, in which compressibility is used in its dual role as a basis for structural inference and classification, extensible motifs are seen to support unsupervised classification and phylogeny reconstruction.Off-line compression based on extensible motifs can be used advantageously to compress and classify biological sequences.
Year
DOI
Venue
2006
10.1186/1748-7188-1-4
Algorithms for Molecular Biology
Keywords
Field
DocType
polynomial time,algorithms,data compression,bioinformatics,sequence motif
Kolmogorov complexity,Computer science,Inference,Motif (music),Suffix tree,Bioinformatics,Time complexity,Data compression,Codebook,Lossless compression
Journal
Volume
Issue
ISSN
1
1
1748-7188
Citations 
PageRank 
References 
16
0.76
10
Authors
3
Name
Order
Citations
PageRank
Alberto Apostolico11441182.20
Matteo Comin219120.94
Laxmi Parida377377.21