DocumentCode
303308
Title
Classification of mouse chromosomes using artificial neural networks
Author
Musavi, M.T. ; Qiao, M. ; Davisson, M.T. ; Akeson, E.C.
Author_Institution
Dept. of Electr. & Comput. Eng., Maine Univ., Orono, ME, USA
Volume
2
fYear
1996
fDate
3-6 Jun 1996
Firstpage
852
Abstract
This paper presents the results of our experiments for classification of mouse chromosomes using a radial basis function (RBF) and a probabilistic neural network (PNN). The fast orthogonal search (FOS) was utilized for training of the RBF network. There were 840 training chromosomes and 540 testing chromosomes. The best classification error rate was recorded at 16.4% for the RBF network. This result is better than the best available result of 18.3% which was achieved with much more training chromosomes
Keywords
cellular biophysics; feedforward neural nets; genetics; pattern classification; artificial neural networks; classification error rate; fast orthogonal search; mouse chromosomes; probabilistic neural network; radial basis function; Artificial neural networks; Biological cells; Cells (biology); Chromosome mapping; Genetics; Humans; Laboratories; Mice; Radial basis function networks; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1996., IEEE International Conference on
Conference_Location
Washington, DC
Print_ISBN
0-7803-3210-5
Type
conf
DOI
10.1109/ICNN.1996.549008
Filename
549008
Link To Document