Abstract
Using effective computer methods to infer potential drug-disease relationships can provide clues for the discovery new uses of old drugs. This paper introduced a Ranking-based k-Nearest Neighbor (Re-KNN) method to drug repositioning for cardiovascular diseases. The main characteristic of the Re-KNN lies in combining conventional KNN algorithm with Ranking SVM (Support Vector Machine) algorithm to get neighbors that are more trustable. By integrating the chemical structural similarity, target-based similarity, side-effect similarity and topological similarity information, Re-KNN method can obtain an improved AUC (Area under ROC Curve) and AUPR (Area under Precision-Recall curve) compared with other methods, which prove the validity and efficiency of multiple features integration.
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References
Adams, C.P., Brantner, V.V.: Estimating the cost of new drug development: is it really $802 million? Health Aff. 25(2), 420–428 (2006)
Ashburn, T.T., Thor, K.B.: Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3(8), 673–683 (2004)
Yang, L., Agarwal, P.: Systematic drug repositioning based on clinical side-effects. PLoS ONE 6(12), e28025 (2011)
Sanseau, P., Agarwal, P., Barnes, M.R., et al.: Use of genome-wide association studies for drug repositioning. Nat. Biotechnol. 30(4), 317–320 (2012)
Clouser, C.L., Patterson, S.E., Mansky, L.M.: Exploiting drug repositioning for discovery of a novel HIV combination therapy. J. Virol. 84(18), 9301–9309 (2010)
Anne, C., James, P., Alistair, B., et al.: Drug repositioning for Alzheimer’s disease. Nat. Rev. Drug Discov. 11(11), 833–846 (2012)
Cheng, F., Liu, C., Jiang, J., et al.: Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol. 8(5), e1002503 (2012)
Alaimo, S., Pulvirenti, A., Giugno, R., et al.: Drug-target interaction prediction through domain-tuned network-based inference. Bioinformatics 29(16), 2004–2008 (2013)
Wu, Z., Wang, Y., Chen, L.: Network-based drug repositioning. Mol. BioSyst. 9(6), 1268–1281 (2013)
Gottlieb, A., Stein, G.Y., Ruppin, E., et al.: Predict: a method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol. 7, 496 (2011)
Chen, H., Zhang, H., Zhang, Z., et al.: Network-based inference methods for drug repositioning. Comput. Math. Methods Med. 2015, 130620 (2015)
Zhang, P., Agarwal, P., Obradovic, Z.: Computational drug repositioning by ranking and integrating multiple data sources. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part III. LNCS, vol. 8190, pp. 579–594. Springer, Heidelberg (2013)
Wang, Y., Chen, S., Deng, N., et al.: Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS ONE 8(11), e78518 (2013)
Chiang, T.H., Lo, H.Y., Lin, S.D.: A ranking-based KNN approach for multi-label classification. JMLR W&CP. 25, 81–96 (2012)
Yu, H., Kim, J., Kim, Y., et al.: An efficient method for learning nonlinear ranking SVM functions. Inf. Sci. 209, 37–48 (2012)
Laarhoven, T.V., Marchiori, E.: Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile. PLoS ONE 8(6), e66952 (2013)
Shi, J.Y., Yiu, S.M., Li, Y., et al.: Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering. Methods 83, 98–104 (2015)
Kanehisa, M., Goto, S.: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 29–34 (2000)
Yap, C.W.: PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 32(7), 1466–1474 (2011)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)
Xia, Z., Wu, L.Y., Zhou, X., et al.: Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol. 4(Suppl 2), S6 (2010)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Depatment of Informatics, Aristotle University of Thessaloniki, Greece (2006)
Biggs, R., Hayton-Williams, D.S.: Epsilon-aminocaproic acid in the treatment of hemophilia. Br. Dent. J. 124(124), 157 (1968)
Leduc, L., Dubois, E., Takser, L., et al.: Dalteparin and low-dose aspirin in the prevention of adverse obstetric outcomes in women with inherited thrombophilia. J. Obstet. Gynaecol. Can. 29(10), 787–793 (2007)
Giardini, O., Cantani, A., Donfrancesco, A.: Vitamin E therapy in homozygous beta-thalassemia. N. Engl. J. Med. 305(11), 644 (1981)
Kerr, C.R., Prystowsky, E.N., Smith, W.M., et al.: Disopyramide phosphate in wolff-parkinson-white syndrome. Am. J. Cardiol. 47(2), 495 (1981)
Chaumais, M.C., Perros, F., Dorfmuller, P., et al.: Nilotinib and imatinib therapy in experimental pulmonary hypertension. Arterioscler. Thromb. Vasc. Biol. 32(6), 1354–1365 (2012)
Acknowledgements
This work was partially supported by National Basic Research Program of China (Grants No. 2012CB910400), the Fundamental Research Funds for the Central Universities (78260026), and National Science and Technology Support Plan Project (2015BAH12F01).
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Tian, X., Xin, M., Luo, J., Jiang, Z. (2016). Using the Ranking-Based KNN Approach for Drug Repositioning Based on Multiple Information. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_31
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