Wavelet analysis in current cancer genome research: A survey

Tao Meng, Ahmed T. Soliman, Mei Ling Shyu, Yimin Yang, Shu Ching Chen, S. S. Iyengar, John S. Yordy, Puneeth Iyengar

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


With the rapid development of next generation sequencing technology, the amount of biological sequence data of the cancer genome increases exponentially, which calls for efficient and effective algorithms that may identify patterns hidden underneath the raw data that may distinguish cancer Achilles' heels. From a signal processing point of view, biological units of information, including DNA and protein sequences, have been viewed as one-dimensional signals. Therefore, researchers have been applying signal processing techniques to mine the potentially significant patterns within these sequences. More specifically, in recent years, wavelet transforms have become an important mathematical analysis tool, with a wide and ever increasing range of applications. The versatility of wavelet analytic techniques has forged new interdisciplinary bounds by offering common solutions to apparently diverse problems and providing a new unifying perspective on problems of cancer genome research. In this paper, we provide a survey of how wavelet analysis has been applied to cancer bioinformatics questions. Specifically, we discuss several approaches of representing the biological sequence data numerically and methods of using wavelet analysis on the numerical sequences.

Original languageEnglish (US)
Article number6654125
Pages (from-to)1442-1459
Number of pages18
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number6
StatePublished - Nov 2013


  • Cancer genome
  • Driver mutation
  • Passenger mutation
  • Wavelet analysis

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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