A New Wavelet-Based Approach for Mass Spectrometry Data Classification
Proteomic patterns can help the diagnosis of the underlying pathological state of an organ such as the ovary, the lung, and the breast, to name a few. An accurate classification of mass spectrometry is a crucial point to establish a reliable diagnosis and decision process regarding the type of cancer. A statistical methodology for classifying mass spectrometry data is proposed. An overview of wavelets, principal component analysis-T2 statistic, and support vector machines is given. The study is performed on low-mass SELDI spectra derived from patients with breast cancer and from normal controls. There are 156 samples where control (normal) patients contribute with 57 samples and 99 samples are cancer. A hyperparameter optimization is conducted to select a support vector machine classification model based on grid search. The performance was evaluated with a k-fold cross validation technique and Monte-Carlo simulation with 100 replications. The average accuracy is 100% with standard error equals to 0. The averages of the sensitivity and specificity are both equal to 100%, as well as the area under the curve. The excellent performance of our proposed method is mainly due to the statistical modeling and the feature extraction procedure proposed.
- Awedat, K., Abdel-Qader, I., & Springstead, J. R. (2016). Mass spectrometry sensing data for robust cancer classification. In Electro Information Technology (EIT), 2016 IEEE International Conference on (pp. 0258–0262). Piscataway: IEEE.Google Scholar
- Das, S. (2001). Filters, wrappers and a boosting-based hybrid for feature selection. In ICML (Vol. 1, pp. 74–81).Google Scholar
- Du, J., Wu, X.-M., Wang, B., Su, H.-J., Ma, K., & Zhang, H.-Q. (2009). Wavelet transform and bagging predictor approaches to cancer identification from mass spectrometry-based proteomic data. In Bioinformatics and Biomedical Engineering, 2009. ICBBE 2009. 3rd International Conference on (pp. 1–4). Piscataway: IEEE.Google Scholar
- Lancashire, L. J., Lemetre, C., & Ball, G. R. (2009). An introduction to artificial neural networks in bioinformatics—application to complex microarray and mass spectrometry datasets in cancer studies. Briefings in Bioinformatics, 10, 315–329. https://doi.org/10.1093/bib/bbp012.CrossRefGoogle Scholar
- P. Datasets for Breast Cancer (2004). http://bioinformatics.mdanderson.org/pubdata.html.
- Schleif, F.-M., Lindemann, M., Diaz, M., Maaß, P., Decker, J., Elssner, T., et al. (2009). Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform. Computing and Visualization in Science, 12(4), 189–199.MathSciNetCrossRefGoogle Scholar
- Sharma, A., & Singh, S. (2016). Neural network for diagnosis of ovarian cancer based on proteomic patterns in serum. Journal of Scientific and Technical Advancements, 2(2), 25–27.Google Scholar
- Wu, J., Ji, Y., Zhao, L., Ji, M., Ye, Z., & Li, S. (2016). A mass spectrometric analysis method based on ppca and svm for early detection of ovarian cancer. Computational and Mathematical Methods in Medicine, 2016, 6169249.Google Scholar