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

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.