Li Xiaoli, Li Jin, Yao Xin
Cercia, School of Computer Science, University of Birmingham B15 2TT, UK.
Comput Biol Med. 2007 Apr;37(4):509-16. doi: 10.1016/j.compbiomed.2006.08.009. Epub 2006 Sep 18.
Recently, mass spectrometry analysis has a become an effective and rapid approach in detecting early-stage cancer. To identify proteomic patterns in serum to discriminate cancer patients from normal individuals, machine-learning methods, such as feature selection and classification, have already been involved in the analysis of mass spectrometry (MS) data with some success. However, the performance of existing machine learning methods for MS data analysis still needs improving. The study in this paper proposes a wavelet-based pre-processing approach to MS data analysis. The approach applies wavelet-based transforms to MS data with the aim of de-noising the data that are potentially contaminated in acquisition. The effects of the selection of wavelet function and decomposition level on the de-noising performance have also been investigated in this study. Our comparative experimental results demonstrate that the proposed de-noising pre-processing approach has potentials to remove possible noise embedded in MS data, which can lead to improved performance for existing machine learning methods in cancer detection.
最近，质谱分析已成为检测早期癌症的一种有效且快速的方法。为了识别血清中的蛋白质组模式以区分癌症患者和正常个体，机器学习方法，如特征选择和分类，已经被用于质谱（MS）数据分析并取得了一定成功。然而，现有用于MS数据分析的机器学习方法的性能仍有待提高。本文的研究提出了一种基于小波的MS数据预处理方法。该方法将基于小波的变换应用于MS数据，旨在对采集过程中可能受到污染的数据进行去噪。本研究还探讨了小波函数选择和分解水平对去噪性能的影响。我们的对比实验结果表明，所提出的去噪预处理方法有潜力去除MS数据中可能存在的噪声，这可以提高现有机器学习方法在癌症检测中的性能。
