Multiscale denoising of biological data: A comparative analysis

M. N. Nounou, H. N. Nounou, N. Meskin, A. Datta, E. R. Dougherty

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Abstract: 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.

Original languageEnglish
Article number6193095
Pages (from-to)1539-1544
Number of pages6
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume9
Issue number5
DOIs
Publication statusPublished - 17 Aug 2012

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Keywords

  • Wavelets
  • copy number data
  • metabolic data
  • multiscale filtering

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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