Liu Yihui
School of Computer Science and Information Technology, Shandong Institute of Light Industry, Shandong, China.
Comput Biol Med. 2009 Sep;39(9):818-23. doi: 10.1016/j.compbiomed.2009.06.012. Epub 2009 Jul 30.
Mass spectrometry is being used to generate protein profiles from human serum, and proteomic data obtained from mass spectrometry have attracted great interest for the detection of early stage cancer. However, high dimensional mass spectrometry data cause considerable challenges. In this paper we propose a feature extraction algorithm based on wavelet analysis for high dimensional mass spectrometry data. A set of wavelet detail coefficients at different scale is used to detect the transient changes of mass spectrometry data. The experiments are performed on 2 datasets. A highly competitive accuracy, compared with the best performance of other kinds of classification models, is achieved. Experimental results show that the wavelet detail coefficients are efficient way to characterize features of high dimensional mass spectra and reduce the dimensionality of high dimensional mass spectra.
质谱分析法正被用于从人血清中生成蛋白质谱，并且从质谱分析法获得的蛋白质组学数据在早期癌症检测方面引起了极大关注。然而，高维质谱数据带来了相当大的挑战。在本文中，我们针对高维质谱数据提出了一种基于小波分析的特征提取算法。利用一组不同尺度下的小波细节系数来检测质谱数据的瞬态变化。实验在两个数据集上进行。与其他类型分类模型的最佳性能相比，实现了极具竞争力的准确率。实验结果表明，小波细节系数是表征高维质谱特征并降低高维质谱维度的有效方法。
