Wavelets in bioinformatics and computational biology: state of art and perspectives.
BACKGROUND
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.
Liò
Pietro
P
Department of Zoology, University of Cambridge, UK. plio@hgmp.mrc.ac.uk
D000465:Algorithms; D019295:Computational Biology / Q000379:methods / Q000639:trends*; D003198:Computer Simulation; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated; D011506:Proteins / Q000737:chemistry; D017422:Sequence Analysis, DNA / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes
D011506:Proteins