NDLI: A novel kernel for sequences classification

Content Provider	IEEE Xplore Digital Library
Author	Chun Yan Zheng-Zhi Wang Qing-Bin Gao Yao-Hua Du
Copyright Year	2005
Description	Author affiliation: Inst. of Autom., Nat. Univ. of Defense Technol., Changsha, China (Chun Yan; Zheng-Zhi Wang; Qing-Bin Gao; Yao-Hua Du)
Abstract	In this paper, a novel kernel, called position weight subsequences kernel (PWSK), is introduced for identifying gene sequences. String subsequences kernel (SSK), which is based on string alignment, performs well for text categorization problems. For gene sequences identification, not only the comprised subsequences but also the positions of them are important. To integrate the position information, the decay factor of match position in SSK was replaced by position weight in PWSK. By doing this, PWSK can integrate both the content and position information of subsequences. This kernel was used for splice site identification and the experimental results demonstrated its efficiency. The sensitivities for donor sites and acceptor sites are 94% and 95%, respectively, and the specificities for them are 96% and 96%. The performance is better than that of SSK. The reason is that the content of sequence alone is not enough to interpret splicing, and it is necessary to include the position information. Compared with the existing approaches, PWSK achieves better sensitivities for both the donor sites and the acceptor sites, and the specificities of them are comparable.
Starting Page	769
Ending Page	773
File Size	759107
Page Count	5
File Format	PDF
ISBN	0780393619
DOI	10.1109/NLPKE.2005.1598840
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Splicing Text categorization Support vector machine classification Genomics Pulse width modulation Dynamic programming Kernel Bioinformatics Testing
Content Type	Text
Resource Type	Article

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