Data Integration for Gene Expression Prediction

Abstract

In computational system biology, one challenging topic is predicting the exact value of gene expression for further meta-analysis. For this, a data integration approach and regression based task are proposed. To improve prediction performance, gene expression data consisted of continuous value is integrated with binary data from miRNA-mRNA regulation pairs by a simple approach. For regression task, a recently introduced method, Relevance Vector Machine (RVM) and linear regression are used. For evaluation, Spearman and Pearson Correlation Coefficients, and Root Mean Squared Error are used. The results we obtain show that the proposed approach can significantly improve the prediction performance. Data integration approach and RVM are promising in many machine learning problems.