Table of Contents Author Guidelines Submit a Manuscript
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.

Linked References

  1. S. H. Nagaraj and A. Reverter, “A Boolean-based systems biology approach to predict novel genes associated with cancer: application to colorectal cancer,” BMC Systems Biology, vol. 5, article 35, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Li, B.-Q. Li, M. Jiang et al., “Prediction and analysis of retinoblastoma related genes through gene ontology and KEGG,” BioMed Research International, vol. 2013, Article ID 304029, 8 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Hosur, J. Xu, J. Bienkowska, and B. Berger, “IWRAP: an interface threading approach with application to prediction of cancer-related protein-protein interactions,” Journal of Molecular Biology, vol. 405, no. 5, pp. 1295–1310, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Aragues, C. Sander, and B. Oliva, “Predicting cancer involvement of genes from heterogeneous data,” BMC Bioinformatics, vol. 9, article 172, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. L. Hakes, J. W. Pinney, D. L. Robertson, and S. C. Lovell, “Protein-protein interaction networks and biology—what's the connection?” Nature Biotechnology, vol. 26, no. 1, pp. 69–72, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. C. von Mering, R. Krause, B. Snel et al., “Comparative assessment of large-scale data sets of protein-protein interactions,” Nature, vol. 417, no. 6887, pp. 399–403, 2002. View at Google Scholar · View at Scopus
  7. B. Schuster-Böckler and A. Bateman, “Protein interactions in human genetic diseases,” Genome Biology, vol. 9, no. 1, article R9, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. R. Sharan, S. Suthram, R. M. Kelley et al., “Conserved patterns of protein interaction in multiple species,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 6, pp. 1974–1979, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. C.-C. Chen, C.-Y. Lin, Y.-S. Lo, and J.-M. Yang, “PPISearch: a web server for searching homologous protein-protein interactions across multiple species,” Nucleic Acids Research, vol. 37, no. 2, pp. W369–W375, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. K. I. Goh, M. E. Cusick, D. Valle, B. Childs, M. Vidal, and A.-L. Barabási, “The human disease network,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 21, pp. 8685–8690, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Ideker and R. Sharan, “Protein networks in disease,” Genome Research, vol. 18, no. 4, pp. 644–652, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Kar, A. Gursoy, and O. Keskin, “Human cancer protein-protein interaction network: a structural perspective,” PLoS Computational Biology, vol. 5, no. 12, Article ID e1000601, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. J.-S. Chen, W.-S. Hung, H.-H. Chan, S.-J. Tsai, and H. Sunny Sun, “In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma,” Bioinformatics, vol. 29, no. 4, pp. 420–427, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. P. F. Jonsson and P. A. Bates, “Global topological features of cancer proteins in the human interactome,” Bioinformatics, vol. 22, no. 18, pp. 2291–2297, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Clancy, E. A. Rødland, S. Nygard, and E. Hovig, “Predicting physical interactions between protein complexes,” Molecular and Cellular Proteomics, vol. 12, no. 6, pp. 1723–1734, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. D. P. Ryan and J. M. Matthews, “Protein-protein interactions in human disease,” Current Opinion in Structural Biology, vol. 15, no. 4, pp. 441–446, 2005. View at Google Scholar
  17. J. Xu and Y. Li, “Discovering disease-genes by topological features in human protein-protein interaction network,” Bioinformatics, vol. 22, no. 22, pp. 2800–2805, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Platzer, P. Perco, A. Lukas, and B. Mayer, “Characterization of protein-interaction networks in tumors,” BMC Bioinformatics, vol. 8, article 224, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Oti, B. Snel, M. A. Huynen, and H. G. Brunner, “Predicting disease genes using protein-protein interactions,” Journal of Medical Genetics, vol. 43, no. 8, pp. 691–698, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Goñi, F. J. Esteban, N. V. de Mendizábal et al., “A computational analysis of protein-protein interaction networks in neurodegenerative diseases,” BMC Systems Biology, vol. 2, article 52, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. Y.-L. Lee, J.-W. Weng, W.-C. Chiang et al., “Investigating cancer-related proteins specific domain interactions and differential protein interactions caused by alternative splicing,” in Proceedings of the 11th IEEE International Conference on Bioinformatics and Bioengineering (BIBE '11), pp. 33–38, Taichung, Taiwan, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. 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. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Ian and E. F. Witten, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 2nd edition, 2005.
  24. Y.-D. Cai, L. Lu, L. Chen, and J.-F. He, “Predicting subcellular location of proteins using integrated-algorithm method,” Molecular Diversity, vol. 14, no. 3, pp. 551–558, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. C. R. Peng, L. Liu, B. Niu et al., “Prediction of RNA-Binding proteins by voting systems,” Journal of Biomedicine and Biotechnology, vol. 2011, Article ID 506205, 8 pages, 2011. View at Publisher · View at Google Scholar
  26. C.-Y. Hor, C.-B. Yang, Z.-J. Yang, and C.-T. Tseng, “Prediction of protein essentiality by the support vector machine with statistical tests,” Evolutionary Bioinformatics Online, vol. 9, pp. 387–416, 2013. View at Publisher · View at Google Scholar
  27. S. K. Dhanda, D. Singla, A. K. Mondal, and G. P. S. Raghava, “DrugMint: a webserver for predicting and designing of drug-like molecules,” Biology Direct, vol. 8, no. 1, article 28, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Wilhelm, “Phenotype prediction based on genome-wide DNA methylation data,” BMC Bioinformatics, vol. 15, no. 1, article 193, 2014. View at Publisher · View at Google Scholar
  29. C.-H. Huang, S.-Y. Chou, and K.-L. Ng, “Improving protein complex classification accuracy using amino acid composition profile,” Computers in Biology and Medicine, vol. 43, no. 9, pp. 1196–1204, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. N. Kurubanjerdjit, C.-H. Huang, Y.-L. Lee, J. J. P. Tsai, and K.-L. Ng, “Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms,” Computers in Biology and Medicine, vol. 43, no. 11, pp. 1645–1652, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. Z.-C. Li, Y.-H. Lai, L.-L. Chen, Y. Xie, Z. Dai, and X.-Y. Zou, “Identifying functions of protein complexes based on topology similarity with random forest,” Molecular BioSystems, vol. 10, no. 3, pp. 514–525, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. A. Chatr-Aryamontri, B.-J. Breitkreutz, S. Heinicke et al., “The BioGRID interaction database: 2013 update,” Nucleic Acids Research, vol. 41, no. 1, pp. D816–D823, 2013. View at Publisher · View at Google Scholar · View at Scopus
  33. L. Wang, Y. Xiong, Y. Sun et al., “HlungDB: an integrated database of human lung cancer research,” Nucleic Acids Research, vol. 38, no. 1, Article ID gkp945, pp. D665–D669, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. R. D. Finn, A. Bateman, J. Clements et al., “Pfam: the protein families database,” Nucleic Acids Research, vol. 42, no. 1, pp. D222–D230, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. H. H. Chan, Identification of novel tumor-associated gene (TAG) by bioinformatics analysis [M.S. thesis], National Cheng Kung University, Tainan City, Taiwan, 2006.
  36. E. Alpaydin, Introduction to Machine Learning, The MIT Press, Cambridge, Mass, USA, 2nd edition, 2011.
  37. I. R. Galatzer-Levy, K.-I. Karstoft, A. Statnikov, and A. Y. Shalev, “Quantitative forecasting of PTSD from early trauma responses: a machine Learning application,” Journal of Psychiatric Research, vol. 59, pp. 68–76, 2014. View at Publisher · View at Google Scholar
  38. C.-H. Huang, M.-H. Wu, P. M.-H. Chang, C.-Y. Huang, and K.-L. Ng, “In silico identification of potential targets and drugs for non-small cell lung cancer,” IET Systems Biology, vol. 8, no. 2, pp. 56–66, 2014. View at Publisher · View at Google Scholar · View at Scopus
  39. T. Barrett, S. E. Wilhite, P. Ledoux et al., “NCBI GEO: archive for functional genomics data sets—update,” Nucleic Acids Research, vol. 41, no. 1, pp. D991–D995, 2013. View at Publisher · View at Google Scholar · View at Scopus
  40. L. J. Su, C. W. Chang, Y. C. Wu et al., “Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme,” BMC Genomics, vol. 8, article 140, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. M. T. Landi, T. Dracheva, M. Rotunno et al., “Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival,” PLoS ONE, vol. 3, no. 2, Article ID e1651, 2008. View at Publisher · View at Google Scholar · View at Scopus
  42. T.-P. Lu, M.-H. Tsai, J.-M. Lee et al., “Identification of a novel biomarker, SEMA5A, for non-small cell lung carcinoma in nonsmoking women,” Cancer Epidemiology Biomarkers and Prevention, vol. 19, no. 10, pp. 2590–2597, 2010. View at Publisher · View at Google Scholar · View at Scopus
  43. T.-Y. W. Wei, C.-C. Juan, J.-Y. Hisa et al., “Protein arginine methyltransferase 5 is a potential oncoprotein that upregulates G1 cyclins/cyclin-dependent kinases and the phosphoinositide 3-kinase/AKT signaling cascade,” Cancer Science, vol. 103, no. 9, pp. 1640–1650, 2012. View at Publisher · View at Google Scholar · View at Scopus
  44. L. R. Barkley and C. Santocanale, “MicroRNA-29a regulates the benzo[a]pyrene dihydrodiol epoxide-induced DNA damage response through Cdc7 kinase in lung cancer cells,” Oncogenesis, vol. 2, article e57, 2013. View at Publisher · View at Google Scholar · View at Scopus
  45. D. Bonte, C. Lindvall, H. Liu, K. Dykema, K. Furge, and M. Weinreich, “Cdc7-Dbf4 kinase overexpression in multiple cancers and tumor cell lines is correlated with p53 inactivation,” Neoplasia, vol. 10, no. 9, pp. 920–931, 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. M. G. Alexandrow, L. J. Song, S. Altiok, J. Gray, E. B. Haura, and N. B. Kumar, “Curcumin: a novel Stat3 pathway inhibitor for chemoprevention of lung cancer,” European Journal of Cancer Prevention, vol. 21, no. 5, pp. 407–412, 2012. View at Publisher · View at Google Scholar · View at Scopus
  47. J. Yang, N. Ramnath, K. B. Moysich et al., “Prognostic significance of MCM2, Ki-67 and gelsolin in non-small cell lung cancer,” BMC Cancer, vol. 6, article 203, 2006. View at Publisher · View at Google Scholar · View at Scopus
  48. E. Langenfeld, M. Deen, E. Zachariah, and J. Langenfeld, “Small molecule antagonist of the bone morphogenetic protein type I receptors suppresses growth and expression of Id1 and Id3 in lung cancer cells expressing Oct4 or nestin,” Molecular Cancer, vol. 12, no. 1, article 129, 2013. View at Publisher · View at Google Scholar · View at Scopus
  49. H. Tang, G. Xiao, C. Behrens et al., “A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients,” Clinical Cancer Research, vol. 19, no. 6, pp. 1577–1586, 2013. View at Publisher · View at Google Scholar · View at Scopus
  50. D. M. MacDermed, N. N. Khodarev, S. P. Pitroda et al., “MUC1-associated proliferation signature predicts outcomes in lung adenocarcinoma patients,” BMC Medical Genomics, vol. 3, article 16, 2010. View at Publisher · View at Google Scholar · View at Scopus
  51. A. Franceschini, D. Szklarczyk, S. Frankild et al., “STRING v9.1: protein-protein interaction networks, with increased coverage and integration,” Nucleic Acids Research, vol. 41, no. 1, pp. D808–D815, 2013. View at Publisher · View at Google Scholar · View at Scopus