Proceedings of the Korean Institute of Intelligent Systems Conference (한국지능시스템학회:학술대회논문집)
- 2003.09a
- /
- Pages.595-598
- /
- 2003
Promoter classification using random generator-controlled generalized regression neural network
- Kim, Kunho (Department of Electronic Engineering, Sejong University) ;
- Kim, Byungwhan (Department of Electronic Engineering, Sejong University) ;
- Kim, Kyungnam (Department of Molecular Biology, Sejong University) ;
- Hong, Jin-Han (DNA Chip Division, Macrogen) ;
- Park, Sang-Ho (DNA Chip Division, Macrogen)
- Published : 2003.09.01
Abstract
A new classifier is constructed by using a generalized regression neural network (GRNN) in conjunction with a random generator (RC). The RG played a role of generating a number of sets of random spreads given a range for gaussian functions in the pattern layer, The range experimentally varied from 0.4 to 1.4. The DNA sequences consisted 4 types of promoters. The performance of classifier is examined in terms of total classification sensitivity (TCS), and individual classification sensitivity (ICS). for comparisons, another GRNN classifier was constructed and optimized in conventional way. Compared GRNN, the RG-GRNN demonstrated much improved TCS along with better ICS on average.
Keywords