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Reseach Article
Dijkstra’s based identification of lung cancer related genes using PPI networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 163 - Number 10 |
| Year of Publication: 2017 |
| Authors: Praveen Tumuluru, Bhramaramba Ravi |
10.5120/ijca2017913496
|
Praveen Tumuluru, Bhramaramba Ravi . Dijkstra’s based identification of lung cancer related genes using PPI networks. International Journal of Computer Applications. 163, 10 ( Apr 2017), 1-5. DOI=10.5120/ijca2017913496
Abstract
Biomedical area of research has grown dynamically for identification of various diseases and prediction of disease, among the most cancer is vital and critical disease caused from various sources of gene mutation. Cancer is one of the most common diseases in the developed world. Cancer arises from the mutation of a normal gene. Mutated genes that cause cancer are called oncogenes The Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and non small cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer- related genes with a shortest path approach in a protein-protein interaction (PPI) network using R tool to set the computation. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways.Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes identified from the PPI community contained extra most cancers genes than the ones recognized from the gene expression profiles. Furthermore, these genes possessed greater purposeful similarity with the recognized cancer genes than those identified from the gene expression profiles. This work proved the performance of the proposed technique and showed promising consequences.
References
- R. Siegel, D. Naishadham, and A. Jemal, “Cancer statistics,” A Cancer Journal for Clinicians, vol. 62,p.p10–29, 2012.
- J. P. van Meerbeeck, D. A. Fennell, and De Ruysscher, “DKM Small-cell lung cancer,” TheLancet, vol. 378, pp. 1741–1755.
- E. Nabieva, K. Jim, A. Agarwal, B. Chazelle, and M. Singh, “Whole-proteome prediction of protein function via graphtheoretic analysis of interaction maps,” Bioinformatics, vol. 21, supplement 1, pp. i302–i310, 2005.
- D. Szklarczyk, A. Franceschini, M. Kuhn et al.,“The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored,” Nucleic Acids Research, vol. 39, no. 1,pp D561–D568, 2011.
- R. Sharan, I. Ulitsky, and R. Shamir, “Network-based prediction of protein function,” MolecularSystems Biology, vol. 3, article 88, 2007.
- P. Bogdanov and A. K. Singh, “Molecular function prediction using neighborhood features,”IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 2, pp. 208–217, 2010.
- Y. A. Kourmpetis, A. D. van Dijk, M. C. Bink, R. C. van Ham, and C. J. ter Braak, “BayesianMarkov Random Field analysis for protein function prediction based on network data,” PLoS One, vol. 5, article e9293, 2010.
- K. L. Ng, J. S. Ciou, and C. H. Huang, “Prediction of protein functions based on function-function correlation relations,” Computers in Biology and Medicine, vol. 40, no. 3, pp. 300–305, 2010.
- U. Karaoz, T. M. Murali, S. Letovsky et al., “Whole-genome annotation by using evidence integration in functional-linkage networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 9, pp. 2888–2893,
- S. Letovsky and S. Kasif, “Predicting protein function from protein/protein interaction data: a probabilistic approach,” Bioinformatics, vol. 19, supplement 1, pp. i197–i204, 2003.
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