Spectral preprocessing for clustering time-series gene expressions

Wentao Zhao, Erchin Serpedin, Edward R. Dougherty

Research output: Contribution to journalArticle

7 Citations (Scopus)


Based on gene expression profiles, genes can be partitioned into clusters, which might be associated with biological processes or functions, for example, cell cycle, circadian rhythm, and so forth. This paper proposes a novel clustering preprocessing strategy which combines clustering with spectral estimation techniques so that the time information present in time series gene expressions is fully exploited. By comparing the clustering results with a set of biologically annotated yeast cell-cycle genes, the proposed clustering strategy is corroborated to yield significantly different clusters from those created by the traditional expression-based schemes. The proposed technique is especially helpful in grouping genes participating in time-regulated processes.

Original languageEnglish
Article number713248
JournalEurasip Journal on Bioinformatics and Systems Biology
Publication statusPublished - 2009
Externally publishedYes


ASJC Scopus subject areas

  • Medicine(all)
  • Computer Science(all)
  • Signal Processing
  • Statistics and Probability
  • General

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