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DNA Sequence Classification Using a Generalized Regression Neural Network and Random Generator  

김성모 (세종대학교 전자공학과)
김근호 (세종대학교 전자공학)
김병환 (세종대학교 전자공학과)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.53, no.7, 2004 , pp. 525-530 More about this Journal
Abstract
A classifier was constructed by using a generalized regression neural network (GRU) and random generator (RG), which was applied to classify DNA sequences. Three data sets evaluated are eukaryotic and prokaryotic sequences (Data-I), eukaryotic sequences (Data-II), and prokaryotic sequences (Data-III). For each data set, the classifier performance was examined in terms of the total classification sensitivity (TCS), individual classification sensitivity (ICS), total prediction accuracy (TPA), and individual prediction accuracy (IPA). For a given spread, the RG played a role of generating a number of sets of spreads for gaussian functions in the pattern layer Compared to the GRNN, the RG-GRNN significantly improved the TCS by more than 50%, 60%, and 40% for Data-I, Data-II, and Data-III, respectively. The RG-GRNN also demonstrated improved TPA for all data types. In conclusion, the proposed RG-GRNN can effectively be used to classify a large, multivariable promoter sequences.
Keywords
Promoter; Generalized Regression Neural Network; Random Generator; Classification;
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Times Cited By KSCI : 1  (Citation Analysis)
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