A Novel Algorithm for Multi-class Cancer Diagnosis on MALDI-TOF Mass Spectra

Mass spectrometry (MS) has been used to generate protein profiles from human serum, and proteomic data obtained from MS have attracted great interest for the detection of cancer. Because MALDI-TOF MS provides high-resolution measurements, the biomarker identification has been limited by the unbalance problem between high-dimensional attributes and small sample-size. To deal with the multi-class problem in cancer prediction and biomarker identification, we propose a fast and robust multi-class cancer classification framework. A novel MS biomarker selection algorithm is provided by utilizing over sampled wavelet transform to extract wavelet coefficients and statistical testing to select features. The multi-class Gentle AdaBoost is used as a classifier due to its efficient classification procedure. Several experiments are deployed on real MALDI-TOF MS data in order to prove the superiority of proposed method compared to previous algorithms. The experimental results show that our proposed framework is an effective tool for analyzing MS data in cancer detection.