A Privacy Preserving and Safety-Aware Semi-supervised Model for Dissecting Cancer Samples

Abstract

Research in cancer genomics has proliferated with the advent of microarray technologies. These technologies facilitate monitoring of thousands of genes in parallel, thus providing insight into disease subtypes and gene functions. Gene expression data obtained from microarray chips are typified by few samples and a large number of genes. Supervised classifiers such as support vector machines (SVM) have been deployed for prediction task. However, insufficient labeled data have resulted in a paradigm shift to semi-supervised learning, in particular, transductive SVM (TSVM). Analysis of gene expression data using TSVM revealed that the performance of the model degenerates in the presence of unlabeled data. We address this issue by using a representative sampling strategy which ensures safety of the classifier even in the presence of unlabeled data. We also address the issue of privacy violation when classifier is shipped to other medical institutes for analysis of shared data. We propose a safety aware and privacy preserving TSVM for classifying cancer subtypes. Performance of TSVM with SVM and accuracy loss of the proposed TSVM are also analyzed.