A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Wavelet based fractal analysis of DNA sequences
MSTD '95 Proceedings of the workshop on Measures of spatio-temporal dynamics
Neural Networks and Genome Informatics
Neural Networks and Genome Informatics
Frequency-Domain Algorithms for Visual Analysis on Genomic Structures in Prokaryotes
CGIV '06 Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation
A Time Series Approach for Identification of Exons and Introns
ICIT '07 Proceedings of the 10th International Conference on Information Technology
On wavelet-based adaptive approach for gene comparison
International Journal of Intelligent Systems Technologies and Applications
Identification of Protein Coding Regions Using the Modified Gabor-Wavelet Transform
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A fast and flexible method for the segmentation of aCGH data
Bioinformatics
Wavelet Analysis of HIV-1 Genome
IACSIT-SC '09 Proceedings of the 2009 International Association of Computer Science and Information Technology - Spring Conference
A short introduction to wavelets and their applications
IEEE Circuits and Systems Magazine
Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural
Expert Systems with Applications: An International Journal
Multiresolution Analysis of DNA Sequences
ICCRD '10 Proceedings of the 2010 Second International Conference on Computer Research and Development
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Waveform Mapping and Time-Frequency Processing of DNA and Protein Sequences
IEEE Transactions on Signal Processing
Wavelet-based feature extraction for DNA microarray classification
Artificial Intelligence Review
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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.