Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 275045, 9 pages
http://dx.doi.org/10.1155/2015/275045
Research Article

Matrix Factorization-Based Prediction of Novel Drug Indications by Integrating Genomic Space

1Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2Beijing University of Chinese Medicine, Beijing 100029, China

Received 10 December 2014; Accepted 6 May 2015

Academic Editor: Francesco Pappalardo

Copyright © 2015 Wen Dai 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. C. P. Adams and V. van Brantner, “Estimating the cost of new drug development: is it really $802 million?” Health Affairs, vol. 25, no. 2, pp. 420–428, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. T. T. Ashburn and K. B. Thor, “Drug repositioning: identifying and developing new uses for existing drugs,” Nature Reviews Drug Discovery, vol. 3, no. 8, pp. 673–683, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. J. T. Dudley, T. Deshpande, and A. J. Butte, “Exploiting drug–disease relationships for computational drug repositioning,” Briefings in Bioinformatics, vol. 12, no. 4, pp. 303–311, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Gottlieb, G. Y. Stein, E. Ruppin, and R. Sharan, “PREDICT: a method for inferring novel drug indications with application to personalized medicine,” Molecular Systems Biology, vol. 7, article 496, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Iorio, R. Bosotti, E. Scacheri et al., “Discovery of drug mode of action and drug repositioning from transcriptional responses,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 33, pp. 14621–14626, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Li and Z. Lu, “Pathway-based drug repositioning using causal inference,” BMC Bioinformatics, vol. 14, supplement 16, article S3, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. L. Yang and P. Agarwal, “Systematic drug repositioning based on clinical side-effects,” PLoS ONE, vol. 6, no. 12, Article ID e28025, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. R. Bell, Y. Koren, and C. Volinsky, “Modeling relationships at multiple scales to improve accuracy of large recommender systems,” in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '07), pp. 95–104, ACM, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Zheng, H. Ding, H. Mamitsuka, and S. Zhu, “Collaborative matrix factorization with multiple similarities for predicting drug-target interactions,” in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1025–1033, Chicago, Ill, USA, August 2013. View at Publisher · View at Google Scholar
  10. M. Gönen, “Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization,” Bioinformatics, vol. 28, no. 18, pp. 2304–2310, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Kong, J. Zhang, X. Mou, and Y. Yang, “Integrating gene expression and protein interaction data for signaling pathway prediction of Alzheimer's disease,” Computational and Mathematical Methods in Medicine, vol. 2014, Article ID 340758, 7 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Zhao and S. Li, “A co-module approach for elucidating drug-disease associations and revealing their molecular basis,” Bioinformatics, vol. 28, no. 7, pp. 955–961, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Li, B. Zhang, and N. Zhang, “Network target for screening synergistic drug combinations with application to traditional Chinese medicine,” BMC Systems Biology, vol. 5, supplement 1, article S10, 2011. View at Google Scholar
  14. Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa, “Prediction of drug-target interaction networks from the integration of chemical and genomic spaces,” Bioinformatics, vol. 24, no. 13, pp. i232–i240, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Yamanishi, M. Kotera, M. Kanehisa, and S. Goto, “Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework,” Bioinformatics, vol. 26, no. 12, pp. i246–i254, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Wu, R. Jiang, M. Q. Zhang, and S. Li, “Network-based global inference of human disease genes,” Molecular Systems Biology, vol. 4, article 189, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. T. S. Keshava Prasad, R. Goel, K. Kandasamy et al., “Human protein reference Database—2009 update,” Nucleic Acids Research, vol. 37, no. 1, pp. D767–D772, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Rendle, “Factorization machines with libFM,” ACM Transactions on Intelligent Systems and Technology, vol. 3, no. 3, article 57, pp. 1–22, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Chen, W. Zhang, Q. Lu, K. Chen, Z. Zheng, and Y. Yu, “SVDFeature: a toolkit for feature-based collaborative filtering,” The Journal of Machine Learning Research, vol. 13, no. 1, pp. 3619–3622, 2012. View at Google Scholar · View at MathSciNet
  20. Y. Zhuang, W.-S. Chin, Y.-C. Juan, and C.-J. Lin, “A fast parallel SGD for matrix factorization in shared memory systems,” in Proceedings of the 7th ACM Conference on Recommender Systems (RecSys '13), pp. 249–256, ACM, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. T. Chen, Z. Zheng, Q. Lu, W. Zhang, and Y. Yu, SVDFeature: User Reference Manual, 2012.