Cancer Biomarkers Classification from SELDI-TOF Mass Spectrometry for Clinical Proteomics: An Approach of Dimensionality Reduction

Abstract

Cancer diagnosis from proteomic profiles has reformed the medical procedures in a significant manner with its enhanced accuracy rate as compared to other ultrasound imaging based process. Efficient classification of suitable cancer biomarkers from proteomic data helps in early diagnosis of cancerous diseases. Mass spectrometry (MS) with protein chip based technology such as the Surface Enhanced Laser Desorption and Ionization Time of Flight (SELDI-TOF) can be used for presence as well as absence of diseases by extracting protein spectra based on m/z ratio and intensity of the protein. For mass spectrometry, efficient and robust feature selection technique is required which can reduce the number of features as much as possible in less time and eliminates any irrelevant or redundant features which can affect the classification performance. This work incorporates an energy based dimensionality reduction for huge data to perform clustering ensemble binary classification of cancer and normal patterns by evaluating biomarker signatures at a higher rate of accuracy. The proposed approach has overcome existing issues especially feature selection based on their discriminatory power. The experimental results show that both SELDI-TOF data sets extracted from WCX2 and H4 chip can be classified with only two features at relatively higher rate with negligible false alarms.