Communal Neural Network for Ovarian Cancer Mutation Classification

Microarrays are being used to express thousands of genes at a time which is helpful to diagnose and cure many diseases with higher accuracy using diagnostic classifiers. However, 90% of the time gene expression datasets contain multiple missing values because of slide scratches, hybridization error, image corruption and etc. These missing values affect classifiers accuracy as most of the classifiers either ignore the missing values of data or replace it with zero value. In this paper we have presented an innovative Communal Neural Network (ComNN) model that estimates the missing values based on genetic correlation principle. The classification accuracy of the proposed system is compared with other well known techniques like Support Vector Machine (SVM), Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and novel Parallel PNN (PPNN) for the classificaton of BRCA1, BRCA2 and Sporadic mutations for ovarian cancer. The results indicate that ComNN outperformed SVM, GRNN, PNN and PPNN when it was cross validated for the aforementioned data containing multiple missing values.