Apriori Gene Set-based Microarray Analysis for Disease Classification Using Unlabeled Data

作     者：Worrawat Engchuan Jonathan Hoyin Chan 

作者机构：Data and Knowledge Engineering Laboratory (D-Lab) School of Information Technology King Mongkut's University of Technology Thonburi Bangkok Thailand 10140 

出 版 物：《Procedia Computer Science》 

年 卷 期：2013年第23卷

页      面：137-145页

主　　题：gene set activity gene expression microarray analysis cancer classification feature selection 

摘      要：Gene set-based microarray analysis allows researchers to better analyze the gene expression data for studying complex diseases like cancer. By transforming gene expression data into another form using gene set information, the biomarkers will have higher discriminative power and should result in more accurate disease classification. This work compares two techniques for applying our previously developed NCFS-i-based method to deal with unlabeled data, i.e. to make predictive diagnosis. Seven cancer datasets that include 4 breast cancer and 3 lung cancer datasets were used in this study. The results show that inferring gene set activity using curated phenotype-correlated genes (PCOGs) sets of training data is a more robust method for applying NCFS-i- based method to work with unlabeled data, providing biologically relevant gene sets.