Issue No. 05 - Sept.-Oct. (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.67
M. N. Nounou , Chem. Eng. Program, Texas A&M Univ. at Qatar, Doha, Qatar
H. N. Nounou , Electr. & Comput. Eng. Program, Texas A&M Univ. at Qatar, Doha, Qatar
N. Meskin , Dept. Electr. Eng., Qatar Univ., Doha, Qatar
A. Datta , Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
E. R. Dougherty , Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
Measured microarray genomic and metabolic data are a rich source of information about the biological systems they represent. For example, time-series biological data can be used to construct dynamic genetic regulatory network models, which can be used to design intervention strategies to cure or manage major diseases. Also, copy number data can be used to determine the locations and extent of aberrations in chromosome sequences. Unfortunately, measured biological data are usually 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 denoising tool. In this work, different batch as well as online multiscale filtering techniques are used to denoise biological data contaminated with white or colored noise. The performances of these techniques are demonstrated and compared to those of some conventional low-pass filters using two case studies. The first case study uses simulated dynamic metabolic data, while the second case study uses real copy number data. Simulation results show that significant improvement can be achieved using multiscale filtering over conventional filtering techniques.
wavelet transforms, biology computing, cellular biophysics, diseases, filtering theory, genetics, genomics, lab-on-a-chip, low-pass filters, signal denoising, time series, simulated dynamic metabolic data, multiscale denoising, microarray genomic data, biological systems, time-series biological data, dynamic genetic regulatory network models, diseases, chromosome sequences, wavelet-based multiscale filtering, online multiscale filtering techniques, colored noise, white noise, conventional low-pass filters, Filtering, Biological system modeling, Noise measurement, DNA, Bioinformatics, Data models, copy number data., Wavelets, multiscale filtering, metabolic data
A. Datta, N. Meskin, H. N. Nounou, M. N. Nounou and E. R. Dougherty, "Multiscale Denoising of Biological Data: A Comparative Analysis," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 1539-1545, 2012.