Signal processing techniques in genomic engineering

Xin Yu Zhang, Fei Chen, Shannon C. Agner, Metin Akay, Zu Hong Lu, Mary Miu Yee Waye, Stephen Kwok Wing Tsui

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

Now that the human genome has been sequenced, the measurement, processing, and analysis of specific genomic information in real time are gaining considerable interest because of their importance to better the understanding of the inherent genomic function, the early diagnosis of disease, and the discovery of new drugs. Traditional methods to process and analyze deoxyribonucleic acid (DNA) or ribonucleic acid data, based on the statistical or Fourier theories, are not robust enough and are time-consuming, and thus not well suited for future routine and rapid medical applications, particularly for emergency cases. In this paper, we present an overview of some recent applications of signal processing techniques for DNA structure prediction, detection, feature extraction, and classification of differentially expressed genes. Our emphasis is placed on the application of wavelet transform in DNA sequence analysis and on cellular neural networks in microarray image analysis, which can have a potentially large effect on the real-time realization of DNA analysis. Finally, some interesting areas for possible future research are summarized, which include a biomodel-based signal processing technique for genomic feature extraction and hybrid multidimensional approaches to process the dynamic genomic information in real time.

Original languageEnglish
Pages (from-to)1822-1832
Number of pages11
JournalProceedings of the IEEE
Volume90
Issue number12
DOIs
StatePublished - 2002

Keywords

  • Biomodel-based method
  • Bionic wavelet transform (BWT)
  • Cellular neural network (CNN)
  • Deoxyribonucleic acid (DNA) microarray
  • Image processing
  • Multidimensional analysis, wavelet transform (WT)

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