TY - GEN
T1 - An iterative GASVM-based method
T2 - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009
AU - Mohamad, Mohd Saberi
AU - Omatu, Sigeru
AU - Deris, Safaai
AU - Yoshioka, Michifumi
PY - 2009
Y1 - 2009
N2 - Microarray technology has provided biologists with the ability to measure the expression levels of thousands of genes in a single experiment. One of the urgent issues in the use of microarray data is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). In this study, we propose an iterative method based on hybrid genetic algorithms to select a near-optimal (smaller) subset of informative genes in classification of the microarray data. The experimental results show that our proposed method is capable in selecting the near-optimal subset to obtain better classification accuracies than other related previous works as well as four methods experimented in this work. Additionally, a list of informative genes in the best gene subsets is also presented for biological usage.
AB - Microarray technology has provided biologists with the ability to measure the expression levels of thousands of genes in a single experiment. One of the urgent issues in the use of microarray data is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). In this study, we propose an iterative method based on hybrid genetic algorithms to select a near-optimal (smaller) subset of informative genes in classification of the microarray data. The experimental results show that our proposed method is capable in selecting the near-optimal subset to obtain better classification accuracies than other related previous works as well as four methods experimented in this work. Additionally, a list of informative genes in the best gene subsets is also presented for biological usage.
KW - Gene selection
KW - Genetic algorithm
KW - Hybrid approach
KW - Iterative method
KW - Microarray data
UR - http://www.scopus.com/inward/record.url?scp=77952560296&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77952560296&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02481-8_26
DO - 10.1007/978-3-642-02481-8_26
M3 - Conference contribution
AN - SCOPUS:77952560296
SN - 3642024807
SN - 9783642024801
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 187
EP - 194
BT - Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, Ambient Assisted Living - 10th Int. Work-Conf. Artificial Neural Networks, IWANN 2009 Workshops, Proceedings
Y2 - 10 June 2009 through 12 June 2009
ER -