Gene expression data classification using locally linear discriminant embedding.
Gene expression data collected from DNA microarray are characterized by a large amount of variables (genes), but with only a small amount of observations (experiments). In this paper, manifold learning method is proposed to map the gene expression data to a low dimensional space, and then explore the intrinsic structure of the features so as to classify the microarray data more accurately. The proposed algorithm can project the gene expression data into a subspace with high intra-class compactness and inter-class separability. Experimental results on six DNA microarray datasets demonstrated that our method is efficient for discriminant feature extraction and gene expression data classification. This work is a meaningful attempt to analyze microarray data using manifold learning method; there should be much room for the application of manifold learning to bioinformatics due to its performance.
Li
Bo
B
Intelligent Computing Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
Zheng
Chun-Hou
CH
Huang
De-Shuang
DS
Zhang
Lei
L
Han
Kyungsook
K
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D016002:Discriminant Analysis; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008297:Male; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis