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Mining Loosely Structured Motifs from Biological Data
November 2008 (vol. 20 no. 11)
pp. 1472-1489
ASCII Text
x 
 
Fabio Fassetti, Gianluigi Greco, Giorgio Terracina, "Mining Loosely Structured Motifs from Biological Data," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 11, pp. 1472-1489, November, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2008.65,
author = {Fabio Fassetti and Gianluigi Greco and Giorgio Terracina},
title = {Mining Loosely Structured Motifs from Biological Data},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {20},
number = {11},
issn = {1041-4347},
year = {2008},
pages = {1472-1489},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.65},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Mining Loosely Structured Motifs from Biological Data
IS - 11
SN - 1041-4347
SP1472
EP1489
EPD - 1472-1489
A1 - Fabio Fassetti,
A1 - Gianluigi Greco,
A1 - Giorgio Terracina,
PY - 2008
KW - Data mining
KW - Bioinformatics (genome or protein) databases
KW - Mining methods and algorithms
VL - 20
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Fabio Fassetti, University of Calabria, Rende
Gianluigi Greco, University of Calabria, Rende
Giorgio Terracina, University of Calabria, Rende
The discovery of information encoded in biological sequences is assuming a prominent role in identifying genetic diseases and in deciphering biological mechanisms. This information is usually encoded in patterns frequently occurring in the sequences, also called motifs. In fact, motif discovery has received much attention in the literature, and several algorithms have already been proposed, which are specifically tailored to deal with motifs exhibiting some kinds of "regular structure". Motivated by biological observations, this paper focuses on the mining of loosely structured motifs, i.e., of more general kinds of motif where several "exceptions" may be tolerated in pattern repetitions. To this end, an algorithm exploiting data structures conceived to efficiently handle pattern variabilities is presented and analyzed. Furthermore, a randomized variant with linear time and space complexity is introduced, and a theoretical guarantee on its performances is proven. Both algorithms have been implemented and tested on real data sets. Despite the ability of mining very complex kinds of pattern, performance results evidence a genome-wide applicability of the proposed techniques.

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Index Terms:
Data mining, Bioinformatics (genome or protein) databases, Mining methods and algorithms
Citation:
Fabio Fassetti, Gianluigi Greco, Giorgio Terracina, "Mining Loosely Structured Motifs from Biological Data," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 11, pp. 1472-1489, Nov. 2008, doi:10.1109/TKDE.2008.65
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