Novel algorithm for coexpression detection in time-varying microarray data sets

Zong Xian Yin, Jung Hsien Chiang

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


When analyzing the results of rnicroarray experiments, biologists generally use unsupervised categorization tools. However, such tools regard each time point as an independent dimension and utilize the euclidean distance to compute the similarities between expressions. Furthermore, some of these methods require the number of clusters to be determined in advance, which is clearly impossible in the case of a new data set. Therefore, this study proposes a novel scheme, designated the Variation-based Coexpression Detection (VCD) algorithm, to analyze the trends of expressions based on their variation over time. The proposed algorithm has two advantages. First, it is unnecessary to determine the number of clusters in advance since the algorithm automatically detects those genes whose profiles are grouped together and creates patterns for these groups. Second, the algorithm features a new measurement criterion for calculating the degree of change of the expressions between adjacent time points and evaluating their trend similarities. Three real-world rnicroarray data sets are employed to evaluate the performance of the proposed algorithm.

Original languageEnglish
Pages (from-to)120-135
Number of pages16
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number1
Publication statusPublished - 2008 Jan 1

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Novel algorithm for coexpression detection in time-varying microarray data sets'. Together they form a unique fingerprint.

Cite this