Wavelets in bioinformatics and computational biology: state of art and perspectives

Bioinformatics. 2003 Jan;19(1):2-9. doi: 10.1093/bioinformatics/19.1.2.

Abstract

Motivation: At a recent meeting, the wavelet transform was depicted as a small child kicking back at its father, the Fourier transform. Wavelets are more efficient and faster than Fourier methods in capturing the essence of data. Nowadays there is a growing interest in using wavelets in the analysis of biological sequences and molecular biology-related signals.

Results: This review is intended to summarize the potential of state of the art wavelets, and in particular wavelet statistical methodology, in different areas of molecular biology: genome sequence, protein structure and microarray data analysis. I conclude by discussing the use of wavelets in modeling biological structures.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Algorithms*
  • Computational Biology / methods
  • Computational Biology / trends*
  • Computer Simulation
  • Models, Biological*
  • Models, Statistical*
  • Oligonucleotide Array Sequence Analysis / methods
  • Pattern Recognition, Automated
  • Proteins / chemistry
  • Sequence Analysis, DNA / methods
  • Signal Processing, Computer-Assisted*
  • Stochastic Processes

Substances

  • Proteins