Abstract
Identifying the response of a cancer patient to a particular therapeutic agent is critical in drug discovery and will significantly facilitate the development of personalized medicine. The publicly available drug response profiles across cell lines provide an alternative way for predicting the response of cancer drugs. In this work, we propose a topology based dual-layer network (TDLN) model to predict drug response based on large-scale cell line experiments. With the Cancer Cell Line Encyclopedia (CCLE), Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutic Response Portal (CTRP) datasets as benchmark datasets, our proposed topology based dual-layer network model outperforms other existing popular approaches and identify some novel indications of known drugs for cancer.
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Acknowledgments
This work was partly supported by National Natural Science Foundation of China (61772368, 61572363, 91530321, 61602347) and Natural Science Foundation of Shanghai (17ZR1445600).
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Huang, S., Zhao, XM. (2018). Prediction of Drug Response with a Topology Based Dual-Layer Network Model. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_1
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DOI: https://doi.org/10.1007/978-3-319-94968-0_1
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