DocumentCode :
511075
Title :
A New Biclustering Method for Gene Expersion Data Based on Adaptive Multi Objective Particle Swarm Optimization
Author :
Lashkargir, Mohsen ; Monadjemi, S. Amirhassan ; Dastjerdi, Ahmad Baraani
Author_Institution :
Dept. of Comput. Eng., Islamic Azad Univ., Yazd, Iran
Volume :
1
fYear :
2009
fDate :
28-30 Dec. 2009
Firstpage :
559
Lastpage :
563
Abstract :
In recent years, with the development of microarray technique, discovery of useful knowledge from microarray data has become very important. Biclustering is a very useful data mining technique for discovering genes which have similar behavior. In microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A multi objective model is very suitable for solving this problem. Our method proposes a Hybrid algorithm which is based on adaptive multi objective particle swarm optimization for discovering biclusters in gene expression data. In our method, we will consider a low level of overlapping among biclusters and as possible, will cover all elements of gene expression matrix. Experimental result in bench mark data base present a significant improvement in overlap among biclusters and coverage of elements in gene expression and quality of biclusters.
Keywords :
biology computing; data mining; genetics; particle swarm optimisation; adaptive multi objective particle swarm optimization; biclustering method; data mining technique; gene expression data; gene expression matrix; microarray technique; Clustering algorithms; Data engineering; Data mining; Evolutionary computation; Gene expression; Knowledge engineering; Particle swarm optimization; Patient monitoring; Robustness; Stochastic processes; biclustering; gene expersion data; multiobjective particle swarm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Electrical Engineering, 2009. ICCEE '09. Second International Conference on
Conference_Location :
Dubai
Print_ISBN :
978-1-4244-5365-8
Electronic_ISBN :
978-0-7695-3925-6
Type :
conf
DOI :
10.1109/ICCEE.2009.245
Filename :
5380183
Link To Document :
بازگشت