Abstract
With the finish of the human genome sequencing and the great progress in molecular biology like proteomics, many established authoritative international biomedical databases are completing continually in recent years. With these opening databases, all kinds of biological molecular networks can be constructed for potential disease gene detection and drug target prediction through network-based approaches. However, most methods do the drug target prediction along with data from only a single source, which have many limitations and tendencies. In this paper, we use multi-source data integrate with datasets from Uniprot, HGNC, COSMIC and DrugBank to do the anti-cancer drug target prediction more comprehensively. We construct Drug-Target network (DT network), Cancer-Gene network (CG network) and Cancer-Drug Interaction network (CDI network) based on the multi-source data we integrate, and do visualizations of the three networks in Cytoscape. In addition, we make an anti-cancer drug target prediction with the method of Random Walks on graphs, one of the most efficient method in biological molecular network analysis by now. Potential anti-cancer drug targets are predicted by calculating the correlation strengths between known cancer gene products and other proteins in CDI network with PersonalRank algorithm. Analysis of the prediction results shows that the potential anti-cancer drug targets we predicted are highly related with cancers both topologically and bio-functionally, which verifies the rationality and availability our method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rual, J.F., Venkatesan, K., Hao, T.: Towards a proteome-scale map of the human protein-protein interaction network. Nature 437(7062), 1173–1178 (2005)
Guney, E., Menche, J., Vidal, M.: Network-based in silico drug efficacy screening. Nat. Commun. 7(10331), 1–13 (2016)
Mestres, J., Gregoripuigjané, E., Valverde, S.: The topology of drug–target interaction networks: implicit dependence on drug properties and target families. Mol. BioSyst. 5(9), 1051–1057 (2009)
Mehmood, R., Elashram, S., Bie, R.: Clustering by fast search and merge of local density peaks for gene expression microarray data. Sci. Rep. 7, 45602 (2017)
Futreal, P.A., Coin, L., Marshall, M.: A census of human cancer genes. Nat. Rev. Cancer 4(3), 177–183 (2004)
Goh, K.I., Cusick, M.E., Valle, D.: The human disease network. Proc. Natl. Acad. Sci. 104(21), 8685–8690 (2007)
Keiser, M.J., Setola, V., Irwin, J.J.: Predicting new molecular targets for known drugs. Nature 462(7270), 175–181 (2009)
Cheng, A.C., Coleman, R.G., Smyth, K.T.: Structure-based maximal affinity model predicts small-molecule druggability. Nat. Biotechnol. 25(1), 71–75 (2007)
Li, Q., Lai, L.: Prediction of potential drug targets based on simple sequence properties. BMC Bioinform. 8(1), 353 (2007)
Wang, Y., Zhao, X.M., Chen, L.: Gene function prediction using labeled and unlabeled data. BMC Bioinform. 9(1), 1–14 (2008)
Campillos, M., Kuhn, M., Gavin, A.C.: Drug target identification using side-effect similarity. Science 321(5886), 263–266 (2008)
Tatonetti, N.P., Liu, T., Altman, R.B.: Predicting drug side-effects by chemical systems biology. Genome Biol. 10(9), 238 (2009)
Zhao, X.M., Iskar, M.: Prediction of drug combinations by integrating molecular and pharmacological data. PLoS Comput. Biol. 7(12), e1002323 (2011)
Wu, H., Li, Y., Miao, Z.: Creative and high-quality image composition based on a new criterion. J. Vis. Commun. Image Represent. 38(C), 100–114 (2016)
Wu, H., Li, Y., Miao, Z.: A new sampling algorithm for high-quality image matting. J. Vis. Commun. Image Represent. 38(C), 573–581 (2016)
Wang, Y.: Repositioning drugs based on molecular network. Shanghai University, Shanghai (2015)
Yu, J., Chen, Y., Ma, L.: On connected target k-coverage in heterogeneous wireless sensor networks. Sensors 16(1), 104 (2015)
Zhang, X., Yu, J., Li, W.: Localized algorithms for Yao graph-based spanner construction in wireless networks under SINR. IEEE/ACM Trans. Netw. 99, 1–14 (2017)
Barabási, A., Gulbahce, N., Loscalzo, J.: Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12(1), 56–68 (2011)
Can, T., Singh, A.K.: Analysis of protein-protein interaction networks using random walks. In: BIOKDD 2005, pp. 61–68. ACM, DBLP, Chicago (2005)
Lovász, L., Lov, L., Erdos, O.P.: Random walks on graphs: a survey. Combinatorics 8(4), 1–46 (1993)
Li, Z., Yang, W., Xie, Z.: Research on PageRank algorithm. Comput. Sci. 38(10A), 185–188 (2011)
Shui, C., Chen, T., Li, H.: Survey on automatic network layouts based on force-directed model. Comput. Eng. Sci. 37(3), 457–465 (2015)
CSDN blog. http://blog.csdn.net/harryhuang1990/article/details/10048383. Accessed 18 Aug 2013
Pan, X.: The Molecular Biology of Gene and Diseases, 1st edn. Chemical Industry Press, Beijing (2014)
Dumontet, C., Jordan, M.A.: Microtubule-binding agents: a dynamic field of cancer therapeutics. Nat. Rev. Drug Discov. 9(10), 790–803 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Li, C. et al. (2018). Cancer-Drug Interaction Network Construction and Drug Target Prediction Based on Multi-source Data. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-94268-1_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94267-4
Online ISBN: 978-3-319-94268-1
eBook Packages: Computer ScienceComputer Science (R0)