Gene Features Selection for Three-Class Disease Classification via Multiple Orthogonal Partial Least Square Discriminant Analysis and S-Plot Using Microarray Data

CORC > 厦门大学 > 医学院－已发表论文

	Gene Features Selection for Three-Class Disease Classification via Multiple Orthogonal Partial Least Square Discriminant Analysis and S-Plot Using Microarray Data
	Yang, Mingxing ; Li, Xiumin ; Li, Zhibin ; Ou, Zhimin ; Liu, Ming ; Liu, Suhuan ; Li, Xuejun ; Yang, Shuyu ; Li XJ(李学军) ; Yang SY(杨叔禹)
刊名	http://dx.doi.org/10.1371/journal.pone.0084253
	2013
关键词	NONNEGATIVE MATRIX FACTORIZATION DIFFERENTIALLY EXPRESSED GENES SUPPORT VECTOR MACHINES MOLECULAR CLASSIFICATION TUMOR CLASSIFICATION CANCER-DIAGNOSIS PLS-DA MODELS MULTICLASS REGRESSION
英文摘要	Xiamen Science and Technology Bureau [3502Z20100001]; National Natural Science Foundation [30973912, 81073113, 81270901]; Motivation: DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Results: Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.
语种	英语
出版者	PUBLIC LIBRARY SCIENCE
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/93413]
专题	医学院－已发表论文
推荐引用方式 GB/T 7714	Yang, Mingxing,Li, Xiumin,Li, Zhibin,et al. Gene Features Selection for Three-Class Disease Classification via Multiple Orthogonal Partial Least Square Discriminant Analysis and S-Plot Using Microarray Data[J]. http://dx.doi.org/10.1371/journal.pone.0084253,2013.
APA	Yang, Mingxing.,Li, Xiumin.,Li, Zhibin.,Ou, Zhimin.,Liu, Ming.,...&杨叔禹.(2013).Gene Features Selection for Three-Class Disease Classification via Multiple Orthogonal Partial Least Square Discriminant Analysis and S-Plot Using Microarray Data.http://dx.doi.org/10.1371/journal.pone.0084253.
MLA	Yang, Mingxing,et al."Gene Features Selection for Three-Class Disease Classification via Multiple Orthogonal Partial Least Square Discriminant Analysis and S-Plot Using Microarray Data".http://dx.doi.org/10.1371/journal.pone.0084253 (2013).