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
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;
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
DOI :
10.1109/ICNN.1996.549008
