Statistical analysis with missing data
Statistical analysis with missing data
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Wavelet Transforms for the Analysis of Microarray Experiments
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
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The expression pattern of a gene across time can be considered as a signal; a microarray experiment is collection of thousands of such signals where due to instrument failure, human errors and technology limitations, values at some time instances are usually missing. Furthermore, in some microarray experiments the gene signals are not sampled at regular time intervals, which renders the direct use of well established frequency-temporal signal analysis approaches such as the wavelet transform problematic. In this work we evaluate a novel multiresolution method, known as the lifting transform to estimate missing values in time series microarray data. Though the lifting transform has been developed to deal with irregularly spaced data its usefulness for the estimation of missing values in microarray data has not been examined in detail yet. In this framework we evaluate the lifting transform against the wavelet transform, a moving average method and a zero imputation on 5 data sets from the cell cycle and the sporulation of the saccharomyces cerevisiae.