Wavelet-based multiscale filtering of genomic data

Measured biological data are a rich source of information about the biological phenomena they represent. For example, time-series genomic or metabolic microarray data can be used to construct dynamic genetic regulatory network models, which can be used to better understand the biological system and to design intervention strategies to cure or manage major diseases. Unfortunately, biological measurements are usually highly contaminated with errors that mask the important features in the data. Therefore, these noisy measurements need to be filtered to enhance their usefulness in practice. Wavelet-based multiscale filtering has been shown to be a powerful data analysis and denoising tool. In this work, different batch as well as online multiscale filtering techniques are used to filter biological data contaminated with white noise. The performances of these multiscale filtering techniques are demonstrated and compared to those of some conventional low pass filters using simulated time series metabolic data. The results of this comparative study show that significant improvement can be achieved using multiscale filtering over conventional filtering methods.