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BioMed Research International
Volume 2015, Article ID 213750, 11 pages
http://dx.doi.org/10.1155/2015/213750
Research Article

ProSim: A Method for Prioritizing Disease Genes Based on Protein Proximity and Disease Similarity

School of Information Science and Engineering, Central South University, Changsha 410083, China

Received 15 December 2014; Accepted 16 January 2015

Academic Editor: Fang-Xiang Wu

Copyright © 2015 Gamage Upeksha Ganegoda 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

Predicting disease genes for a particular genetic disease is very challenging in bioinformatics. Based on current research studies, this challenge can be tackled via network-based approaches. Furthermore, it has been highlighted that it is necessary to consider disease similarity along with the protein’s proximity to disease genes in a protein-protein interaction (PPI) network in order to improve the accuracy of disease gene prioritization. In this study we propose a new algorithm called proximity disease similarity algorithm (ProSim), which takes both of the aforementioned properties into consideration, to prioritize disease genes. To illustrate the proposed algorithm, we have conducted six case studies, namely, prostate cancer, Alzheimer’s disease, diabetes mellitus type 2, breast cancer, colorectal cancer, and lung cancer. We employed leave-one-out cross validation, mean enrichment, tenfold cross validation, and ROC curves to evaluate our proposed method and other existing methods. The results show that our proposed method outperforms existing methods such as PRINCE, RWR, and DADA.