DocumentCode
2933960
Title
Application of Data Mining in Research of Avian Influenza Virus Cross-Species Infection
Author
Li, Shasha ; Zhou, Yuanchun ; Li, Jianhui ; Luo, Ze ; Kou, Zheng ; Li, Tianxian ; Yan, Baoping
Author_Institution
Comput. Network Inf. Center, Beijing, China
fYear
2011
fDate
5-8 Dec. 2011
Firstpage
89
Lastpage
96
Abstract
Avian Influenza Virus (AIV) has already crossed species barriers to infect humans, but the reason for avian flu cross-species infection is unknown. A lot of biology experiment accumulated tens of thousands of biological information data. As a new technology based on database and statistics, data mining provides unprecedented data analysis tool to biologists and powerful means to gene and protein´s analysis and extraction. In this paper, we applied feature-based clustering and classification method to the research of AIV cross-species infection, finding useful patterns, and created a web-based early warning system. We also applied entropy plot and regression analysis to discover host-associated sites in AIV cross-species infection, and got the key sites that differentiate human versus avian influenza using the same method. Compared the two sets, we expected to discover some rules about the mutation trend.
Keywords
Internet; alarm systems; data analysis; data mining; entropy; genetics; medical computing; microorganisms; pattern classification; pattern clustering; proteins; regression analysis; Web-based early warning system; avian influenza virus cross-species infection; classification method; data analysis tool; data mining; database; entropy plot; feature-based clustering; gene; mutation trend; protein analysis; regression analysis; statistics; Entropy; Feature extraction; Humans; Influenza; Proteins; Wavelet packets; avian influenza virus; cross-species infection; data mining; key site discovery; statistic analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
E-Science (e-Science), 2011 IEEE 7th International Conference on
Conference_Location
Stockholm
Print_ISBN
978-1-4577-2163-2
Type
conf
DOI
10.1109/eScience.2011.21
Filename
6123264
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