Clustering-based multi-class classification of complex disease

Pathway activity data transformed from gene expression profiles may be used to identify tumors, complex diseases progression, and cellular response to stimuli, and so on. Previous researches utilized data mining techniques on pathway activity data to distinguish subjects or to predict the phenotype outcome of subject directly. However, in the multi-class classification, learning those data mixing with population from different groups may result in contaminated model as excessive information is presented. This research, we use a two-stage approach applying clustering to homogenize training data before building the classification model. Hierarchical Clustering is used as a clustering method and Random Forest is used as classifier for evaluating the performance of the proposed method. The results are promising and show that using a clustering technique before classifying improves classification performance in general.