A multi-information based gene scoring method for analysis of gene expression data

Hsieh Hui Yu*, Vincent Shin-Mu Tseng, Jiin Haur Chuang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Hepatitis B virus (HBV) infection is a worldwide health problem, with more than 1 million people died from liver cirrhosis and hepatocellular carcinoma (HCC) each year. HBV infection could result in the progression from normal to serious cirrhosis which is insidious and asymptomatic in most of the cases. The recent development of DNA microarray technology provides biomedical researchers with a molecular sight to observe thousands of genes simultaneously. How to efficiently extract useful information from these large-scale gene expression data is an important issue. Although there exist a number of interesting researches on this issue, they used to deploy some complicated statistical hypotheses. In this paper, we propose a multi-information-based methodology to score genes based on the microarray expressions. The concept of multi-information here is to combine different scoring functions in different tiers for analyzing gene expressions. The proposed methods can rank the genes according to the degree of relevance to the targeted diseases so as to form a precise prediction base. The experimental results show that our approach delivers accurate prediction through the assessment of QRT-PRC results.

Original languageEnglish
Title of host publicationTransactions on Computational Systems Biology V
Number of pages15
StatePublished - 1 Dec 2006
Event2005 IEEE International Conference on Granular Computing - Beijing, China
Duration: 25 Jul 200527 Jul 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4070 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2005 IEEE International Conference on Granular Computing

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