a hybrid feature subset selection algorithm for analysis of high correlation proteomic data

Pathological changes within an organ can be reflected as proteomic patterns in biological fluids such as plasma, serum, and urine.the surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (seldi-tof ms) has been used to generateproteomic profiles from biological fluids. mass spectrometry yields redundant noisy data that the most data points are irrelevantfeatures for differentiating between cancer and normal cases. in this paper, we have proposed a hybrid feature subset selectionalgorithm based on maximum-discrimination and minimum-correlation coupled with peak scoring criteria. our algorithm has beenapplied to two independent seldi-tof ms datasets of ovarian cancer obtained from the nci-fda clinical proteomics databank. theproposed algorithm has used to extract a set of proteins as potential biomarkers in each dataset. we applied the linear discriminateanalysis to identify the important biomarkers. the selected biomarkers have been able to successfully diagnose the ovarian cancerpatients from the noncancer control group with an accuracy of 100%, a sensitivity of 100%, and a specificity of 100% in the twodatasets. the hybrid algorithm has the advantage that increases reproducibility of selected biomarkers and able to find a small set ofproteins with high discrimination power.