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 generate proteomic profiles from biological fluids. Mass spectrometry yields redundant noisy data that the most data points are irrelevant features for differentiating between cancer and normal cases. In this paper, we have proposed a hybrid feature subset selection algorithm based on maximum-discrimination and minimum-correlation coupled with peak scoring criteria. Our algorithm has been applied to two independent SELDI-TOF MS datasets of ovarian cancer obtained from the NCI-FDA clinical proteomics databank. The proposed algorithm has used to extract a set of proteins as potential biomarkers in each dataset. We applied the linear discriminate analysis to identify the important biomarkers. The selected biomarkers have been able to successfully diagnose the ovarian cancer patients from the noncancer control group with an accuracy of 100%, a sensitivity of 100%, and a specificity of 100% in the two datasets. The hybrid algorithm has the advantage that increases reproducibility of selected biomarkers and able to find a small set of proteins with high discrimination power

A New Hybrid Feature Subset Selection Algorithm for the Analysis of Ovarian Cancer Data Using Laser Mass Spectrum

Introduction: Amajor problem in the treatment of cancer is the lack of an appropriate method for the early diagnosis of the disease. The chemical reaction within an organ may be reflected in the form of proteomic patterns in the serum, sputum, or urine. Laser mass spectrometry is a valuable tool for extracting the proteomic patterns from biological samples. A major challenge in extracting such patterns is the optimum selection of feature subset from mass spectrum data. Materials and Methods: In this research, the data corresponding to proteomic patterns of serum from patients with ovarian cancer was analyzed in two independent groups. Using a mathematical model, the baseline and electrical noises were eliminated in the preprocessing stage with subsequent normalization of mass spectra. The proposed method uses a hybrid algorithm based on a statistical test and Bhattacharyya distance measure. Using the final prediction error criteria, the best feature subset was selected from 15154 data points while maintaining the resolution and the valuable information. The selected feature subset was then used for the detection of biomarkers within the mass spectrum. Results: Using the method of k-fold cross validation, the samples under study were divided into two sets called the learning and test. Using the least threshold value, the points having significance difference (p-value < 0.05) were selected. The best subset was then extracted from the remaining points such that it would have the maximum information content. By doing so, the number of input variables was reduced from 15154 to 80 points. In the next step, 16 and 6 biomarkers were selected for the two independent dataset. The obtained results show accuracy, specificity as well as sensitivity of 100%. Discussion and Conclusion: To diagnose a disease in medicine is an example of pattern recognition in engineering and physical science. In this paper, a filter approach is introduced for feature subset selection which extracts appropriate features in the input space by using the combination of statistical method and distance measure based on information criteria. The result of this study emphasizes that the use of combination approach in feature extraction and selection in high dimensional data can appropriately separate the pattern classes in addition to maintaining the information content