Signal processing techniques in genomic engineering
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AbstractNow 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. © 2002 IEEE.
All Author(s) ListZhang X.-Y., Chen F., Agner S.C., Akay M., Lu Z.-H., Waye M.M.Y., Tsui S.K.-W.
Journal nameProceedings of the IEEE
Volume Number90
Issue Number12
PublisherInstitute of Electrical and Electronics Engineers
Place of PublicationUnited States
Pages1822 - 1832
LanguagesEnglish-United Kingdom
KeywordsBiomodel-based method, Bionic wavelet transform (BWT), Cellular neural network (CNN), Deoxyribonucleic acid (DNA) microarray, Image processing, Multidimensional analysis, wavelet transform (WT)

Last updated on 2021-12-01 at 00:55