NDLI: Solving the small sample size problem in protein subcellular localization prediction

Content Provider	IEEE Xplore Digital Library
Author	Tong Wang Xiaoxia Cao Tian Xia Zhizhen Yang
Copyright Year	2012
Description	Author affiliation: Inst. of Comput. & Inf., Shanghai Second Polytech. Univ., Shanghai, China (Tong Wang; Xiaoxia Cao; Tian Xia; Zhizhen Yang)
Abstract	In this paper, a new system is proposed to improve the performance of protein subcellular localization prediction. First of all, the protein sequences are quantized into a high dimension space using an effective sequence encoding scheme. However, the problem caused by such representation is small sample size problem, where the data dimension is much larger than the sample size. To sort out this problem, a new dimension reduction algorithm is introduced. It extracts the essential features from the high dimension feature space and does not suffer from small sample size problem. Then, an efficient classifier is employed to recognize the subcellular localization of proteins according to the new features after dimension reduction.
Starting Page	915
Ending Page	918
File Size	223176
Page Count	4
File Format	PDF
ISBN	9781467311830
e-ISBN	9781467311847
DOI	10.1109/BMEI.2012.6513152
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	small sample size problem prediction system manifold learning
Content Type	Text
Resource Type	Article

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Solving the small sample size problem in protein subcellular localization prediction