Prediction of Cancer using Network Topological Features



Several data mining methods have been applied to explore biological data and understand the mechanisms that regulate genetic and metabolic diseases. The underlying hypothesis is that the identification of signatures can help the clinical identification of diseased tissues. Under this principle many different methodologies have been tested mostly using unsupervised methods. A common trend consists in combining the information obtained from gene expression and protein-protein interaction networks analyses or, more recently, building series of complex networks to model system dynamics. Despite the positive results that these works present, they typically fail to generalize out of sample datasets. In this paper we describe a supervised classification approach, with a new methodology for extracting the network topology dynamics embedded in a disease system, to improve the capacity of cancer prediction, using exclusively the topological properties of biological networks as features. Four mi croarrays datasets were used, for testing and validation, three from breast cancer experiments and one from a liver cancer experiment. The obtained results corroborate the potential of the proposed methodology to predict a certain type of cancer and the necessity of applying different classification models to different types of cancer.




Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies 2016


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