Gene expression data classification using locally linear discriminant embedding

Bo Li, Chun Hou Zheng, De Shuang Huang, Lei Zhang, Kyungsook Han

Research output: Journal article publicationJournal articleAcademic researchpeer-review

33 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)802-810
Number of pages9
JournalComputers in Biology and Medicine
Issue number10
Publication statusPublished - 1 Oct 2010


  • Classification
  • Gene expression data
  • Locally linear discriminant embedding.
  • Manifold learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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