TotalPLS: Local Dimension Reduction for Multicategory Microarray Data

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	TotalPLS: Local Dimension Reduction for Multicategory Microarray Data
	You, Wenjie ; Yang, Zijiang ; Yuan, Mingshun ; Ji, Guoli ; Ji GL(吉国力)
刊名	http://dx.doi.org/10.1109/THMS.2013.2288777
	2014
关键词	SUPPORT VECTOR MACHINES PARTIAL LEAST-SQUARES FEATURE SUBSET-SELECTION GENE-EXPRESSION DATA CANCER CLASSIFICATION MUTUAL INFORMATION FEATURE-EXTRACTION SVM-RFE REGRESSION EFFICIENT
英文摘要	Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China [61174161, 61201358, 61203176]; Natural Science Foundation of Fujian Province of China [2012J01154]; specialized Research Fund for the Doctoral Program of Higher Education of China [20130121130004, 20120121120038]; Key Research Project of Xiamen City of China [3502Z20123014]; Fundamental Research Funds for the Central Universities in China (Xiamen University) [2011121047, 2013121025, CBX2013015]; Dimension reduction is an important topic in data mining, which is widely used in the areas of genetics, medicine, and bioinformatics. We propose a new local dimension reduction algorithm TotalPLS that operates in a unified partial least squares (PLS) framework and implement an information fusion of PLSbased feature selection and feature extraction. This paper focuses on extracting the potential structure hidden in high-dimensional multicategory microarray data, and interpreting and understanding the results provided by the potential structure information. First, we propose using PLS-based recursive feature elimination (PLSRFE) in multicategory problems. Then, we perform feature importance analysis based on PLSRFE for high-dimensional microarray data to determine the information feature (biomarkers) subset, which relates to the studied tumor subtypes problem. Finally, PLS-based supervised feature extraction is conducted on the selected specific genes subset to extract comprehensive features that best reflect the nature of classification to have a discriminating ability. The proposed algorithm is compared with several state-of-the-art methods using multiple high-dimensional multicategory microarray datasets. Our comparison is performed in terms of recognition accuracy, relevance, and redundancy. Experimental results show that the algorithm proposed by us can improve the recognition rate and computational efficiency. Furthermore, mining potential structure information improves the interpretability and understandability of recognition results. The proposed algorithm can be effectively applied tomicroarray data analysis for the discovery of gene coexpression and coregulation.
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92736]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	You, Wenjie,Yang, Zijiang,Yuan, Mingshun,et al. TotalPLS: Local Dimension Reduction for Multicategory Microarray Data[J]. http://dx.doi.org/10.1109/THMS.2013.2288777,2014.
APA	You, Wenjie,Yang, Zijiang,Yuan, Mingshun,Ji, Guoli,&吉国力.(2014).TotalPLS: Local Dimension Reduction for Multicategory Microarray Data.http://dx.doi.org/10.1109/THMS.2013.2288777.
MLA	You, Wenjie,et al."TotalPLS: Local Dimension Reduction for Multicategory Microarray Data".http://dx.doi.org/10.1109/THMS.2013.2288777 (2014).