A multi-information based gene scoring model with applications on analysis of hepatocellular carcinoma

Hepatitis B virus (HBV) infection is a major health problem worldwide, with more than 1 million people died each year from liver cirrhosis and hepatocellular carcinoma (HCC). The recent development of DNA microarray technology allows us to simultaneously analyze thousands of genes that are differentially expressed in different clinical status. However, the large amount of data has far exceeded our human ability for comprehension without powerful analysis tools. We proposed a methodology to score genes based on the microarray expressions, with the aim to extract interesting genes related to targeted diseases. The methodology consists of preprocessing steps and multi-information based gene scoring methods that can rank the genes according to the degree of relevance to the analysis target. The proposed methodology is applied for analysis of liver cirrhosis and hepatocellular carcinoma (HCC). The experimental results show that our approach has high predictive power through the assessment of QRT-PRC results.