Signal processing techniques in genomic engineering
Xin-Yun Zhang
Fei Chen
Yuan-Ting Zhang
Agner, S.C.
Akay, M.
Zu-Hong Lu
Waye, M.M.Y.
Tsui, S.K.-W.
Joint Res. Center for Biomed. Eng., Chinese Univ. of Hong Kong;
This paper appears in: Proceedings of the IEEE
Publication Date: Dec 2002
Volume: 90,
Issue: 12
On page(s): 1822- 1833
ISSN: 0018-9219
INSPEC Accession Number: 7472698
Digital Object Identifier: 10.1109/JPROC.2002.805308
Posted online: 2002-12-10 17:14:50.0
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.
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