Microarray data classification using the spectral-feature-based TLS ensemble algorithm

Zhan Li Sun, Han Wang, Wai Shing Lau, Gerald Seet, Danwei Wang, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


The reliable and accurate identification of cancer categories is crucial to a successful diagnosis and a proper treatment of the disease. In most existing work, samples of gene expression data are treated as one-dimensional signals, and are analyzed by means of some statistical signal processing techniques or intelligent computation algorithms. In this paper, from an image-processing viewpoint, a spectral-feature-based Tikhonov-regularized least-squares (TLS) ensemble algorithm is proposed for cancer classification using gene expression data. In the TLS model, a test sample is represented as a linear combination of the atoms of a dictionary. Two types of dictionaries, namely singular value decomposition (SVD)-based eigenassays and independent component analysis (ICA)-based eigenassays, are proposed for the TLS model, and both are extracted via a two-stage approach. The proposed algorithm is inspired by our finding that, among these eigenassays, the categories of some of the testing samples can be assigned correctly by using the TLS models formed from some of the spectral features, but not for those formed from the original samples only. In order to retain the positive characteristics of these spectral features in making correct category assignments, a strategy of classifier committee learning (CCL) is designed to combine the results obtained from the different spectral features. Experimental results on standard databases demonstrate the feasibility and effectiveness of the proposed method.
Original languageEnglish
Article number6846351
Pages (from-to)289-299
Number of pages11
JournalIEEE Transactions on Nanobioscience
Issue number3
Publication statusPublished - 1 Sept 2014


  • Classifier combination
  • Fourier transform
  • Gabor filter
  • microarray data classification
  • sparse representation

ASJC Scopus subject areas

  • Biotechnology
  • Medicine (miscellaneous)
  • Bioengineering
  • Biomedical Engineering
  • Pharmaceutical Science
  • Computer Science Applications
  • Electrical and Electronic Engineering


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