DocumentCode
2723319
Title
Biological Sequence Mining Using Plausible Neural Network and its Application to Exon/intron Boundaries Prediction
Author
Li, Kuochen ; Chang, Dar-Jen ; Rouchka, Eric ; Yuan Yan Chen
Author_Institution
CECS, Louisville Univ., KY
fYear
2007
fDate
1-5 April 2007
Firstpage
165
Lastpage
169
Abstract
Biological sequence usually contains yet to find knowledge, and mining biological sequences usually involves a huge dataset and long computation time. Common tasks for biological sequence mining are pattern discovery, classification and clustering. The newly developed model, plausible neural network (PNN), provides an intuitive and unified architecture for such a large dataset analysis. This paper introduces the basic concepts of the PNN, and explains how it is applied to biological sequence mining. The specific task of biological sequence mining, exon/intron prediction, is implemented by using PNN. The experimental results show the capability of solving biological sequence mining tasks using PNN
Keywords
biology computing; data analysis; data mining; neural nets; pattern classification; pattern clustering; biological sequence mining; exon/intron boundaries prediction; large dataset analysis; pattern classification; pattern clustering; pattern discovery; plausible neural network; Biological information theory; Biological system modeling; Biology computing; Computational biology; Computer architecture; Mutual information; Neural networks; Neurons; Predictive models; Sequences;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Bioinformatics and Computational Biology, 2007. CIBCB '07. IEEE Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0710-9
Type
conf
DOI
10.1109/CIBCB.2007.4221219
Filename
4221219
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