Minimum Redundancy Maximum Relevance(mRMR) Based Feature Selection Technique for Pattern Classification System

Feature Selection is an important hurdle in classification systems. We study how to select good features by making the covariance matrix of each sample data set and extracting the features from it .Then, we try to find out the length of each sample by finding the error rate .We perform experimental comparison of our algorithm and other methods using two data sets(binary and functional) and three different classifiers(support vector machine, linear discriminant analysis and naïve Bayes).The results show that the MRMR features are less correlated with each other as compared to other methods and hence improves the classification accuracy

Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis

Background: The distribution of the chromatin-associatedproteins plays a key role in directing nuclear function. Previously, wedeveloped an image-based method to quantify the nuclear distributions ofproteins and showed that these distributions depended on the phenotype ofhuman mammary epithelial cells. Here we describe a method that creates ahierarchical tree of the given cell phenotypes and calculates thestatistical significance between them, based on the clustering analysisof nuclear protein distributions. Results: Nuclear distributions ofnuclear mitotic apparatus protein were previously obtained fornon-neoplastic S1 and malignant T4-2 human mammary epithelial cellscultured for up to 12 days. Cell phenotype was defined as S1 or T4-2 andthe number of days in cultured. A probabilistic ensemble approach wasused to define a set of consensus clusters from the results of multipletraditional cluster analysis techniques applied to the nucleardistribution data. Cluster histograms were constructed to show how cellsin any one phenotype were distributed across the consensus clusters.Grouping various phenotypes allowed us to build phenotype trees andcalculate the statistical difference between each group. The resultsshowed that non-neoplastic S1 cells could be distinguished from malignantT4-2 cells with 94.19 percent accuracy; that proliferating S1 cells couldbe distinguished from differentiated S1 cells with 92.86 percentaccuracy; and showed no significant difference between the variousphenotypes of T4-2 cells corresponding to increasing tumor sizes.Conclusion: This work presents a cluster analysis method that canidentify significant cell phenotypes, based on the nuclear distributionof specific proteins, with high accuracy