DocumentCode :
2770561
Title :
Applying RBF Neural Networks to Cancer Classification Based on Gene Expressions
Author :
Chu, Feng ; Wang, Lipo
Author_Institution :
Nanyang Technol. Univ., Singapore
fYear :
0
fDate :
0-0 0
Firstpage :
1930
Lastpage :
1934
Abstract :
Accurate classification of cancers based on microarray gene expressions is very important for doctors to choose a proper treatment. In this paper, we apply a novel radial basis function (RBF) neural network that allows for large overlaps among the hidden kernels of the same class to this problem. We tested our RBF network in three data sets, i.e., the lymphoma data set, the small round blue cell tumors (SRBCT) data set, and the ovarian cancer data set. The results in all the three data sets show that our RBF network is able to achieve 100% accuracy with much fewer genes than the previously published methods did.
Keywords :
cancer; genetics; medical computing; radial basis function networks; RBF neural networks; cancer classification; lymphoma data set; microarray gene expressions; ovarian cancer; radial basis function; small round blue cell tumors; Cancer; Gene expression; Kernel; Neoplasms; Neural networks; Radial basis function networks; Statistical analysis; Support vector machine classification; Support vector machines; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
Type :
conf
DOI :
10.1109/IJCNN.2006.246936
Filename :
1716346
Link To Document :
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