Improvement of Survival Prediction from Gene Expression Profiles by Mining of Prior Knowledge

Abstract

Identification of a small set of discriminative genes is a crucial step for effective prediction of disease or patient survival based on microarray gene expression data. Previous approaches to this problem are mainly based on analyzing differential gene expression data. In this work, an additional step is introduced to take advantage of prior knowledge about the relation of genes and a disease. In the proposed approach, keyword scanning of human proteins at the Swissprot database is performed to select genes related to the disease of interest followed by analysis of differential gene expressions. In results obtained on lung cancer data where a differential expression-based selection of genes is fairly inaccurate, our prior knowledge mining based approach offered a large improvement of prediction accuracy (0.74 vs. 0.58 ROC curve when using 20 genes). Furthermore, experimental results on a breast cancer dataset, where prediction based on differential gene expression alone was quite accurate can be further improved by integrating with our new approach.