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BioMed Research International
Volume 2015, Article ID 312047, 15 pages
http://dx.doi.org/10.1155/2015/312047
Research Article

Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms

1Department of Computer Science and Information Engineering, National Formosa University, 64 Wen-Hwa Road, Huwei, Yunlin 63205, Taiwan
2Department of Biomedical Informatics, Asia University, Wufeng Shiang, Taichung 41354, Taiwan
3Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan

Received 2 December 2014; Revised 25 February 2015; Accepted 3 March 2015

Academic Editor: Xia Li

Copyright © 2015 Chien-Hung Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues’s method by employing the protein-protein interaction (PPI) data, domain-domain interaction (DDI) data, weighted domain frequency score (DFS), and cancer linker degree (CLD) data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I) using individual algorithm, (II) combining algorithms, and (III) combining the same classification types of algorithms. When compared with Aragues’s method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.