title: Network-based Prediction of Cancer under Genetic Storm.
abstract: Classification of cancer patients using traditional methods is a challenging task in the medical practice. Owing to rapid advances in microarray technologies, currently expression levels of thousands of genes from individual cancer patients can be measured. The classification of cancer patients by supervised statistical learning algorithms using the gene expression datasets provides an alternative to the traditional methods. Here we present a new network-based supervised classification technique, namely the NBC method. We compare NBC to five traditional classification techniques (support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), C4.5, and random forest (RF)) using 50-300 genes selected by five feature selection methods. Our results on five large cancer datasets demonstrate that NBC method outperforms traditional classification techniques. Our analysis suggests that using symmetrical uncertainty (SU) feature selection method with NBC method provides the most accurate classification strategy. Finally, in-depth analysis of the correlation-based co-expression networks chosen by our network-based classifier in different cancer classes shows that there are drastic changes in the network models of different cancer types.
authors.affiliation: Department of Mathematics, Colgate University, Hamilton, NY, USA. ; Department of Biology, Colgate University, Hamilton, NY, USA.
authors.forename: Ahmet
authors.initials: A
authors.lastname: Ay
authors.affiliation: Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.
authors.forename: Dihong
authors.initials: D
authors.lastname: Gong
authors.affiliation: Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.
authors.forename: Tamer
authors.initials: T
authors.lastname: Kahveci
keywords: cancer classification; comparison of classification techniques; comparison of feature selection techniques; feature selection; network-based cancer prediction