Liò Pietro
Department of Zoology, University of Cambridge, UK.
Bioinformatics. 2003 Jan;19(1):2-9. doi: 10.1093/bioinformatics/19.1.2.
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.
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.
在最近一次会议上，小波变换被描述为一个向其“父亲”傅里叶变换“还手”的小孩。在捕捉数据本质方面，小波比傅里叶方法更高效、更快。如今，人们对在生物序列分析和分子生物学相关信号中使用小波的兴趣与日俱增。
本综述旨在总结当前最先进的小波，尤其是小波统计方法在分子生物学不同领域的潜力：基因组序列、蛋白质结构和微阵列数据分析。最后，我将讨论小波在生物结构建模中的应用。
