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Group-sparse Modeling Drug-kinase Networks for Predicting Combinatorial Drug Sensitivity in Cancer Cells 会议论文 期刊论文

编号：

ee8fcf44-a3cb-465e-8ec4-c4aa72f98d77
作者：

Liu, Hui^[1,2] Luo, Libo^[1] Cheng, Zhanzhan^[3,4] Sun, Jianjiang^[3,4] Guan, Jihong^[5] Zheng, Jie^[6] Zhou, Shuigeng^[1,3,4]
语种：

English
期刊：

CURRENT BIOINFORMATICS ISSN：1574-8936 2018 年 13 卷 5 期 (437 - 443)
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关键词：

Drug combination sparse representation group structure drug sensitivity cancer cells drug-kinase
摘要：

Background: Due to the intrinsic compensatory mechanism and cross-talks mong cellular signaling pathways, single-target drugs often fail to inhibit the survival pathways in cancer cells. Some multi-target combination drugs have demonstrated their high sensitivities and low side effects in cancer therapies, and thus drawn intensive attentions from researchers and pharmaceutical enterprises. Method: Although a few computational methods have been developed to infer combination drug sensitivities based on drug-kinase interactions, they either depend on the binarization of drug-kinase binding affinities, which would lead to the loss of weak drug-target inhibitions known to affect significantly the anticancer effects, or disregard the functional group structure among the kinases involved in cancer signalling pathways. In this paper, we employed a sparse linear model, uncertain group sparse representation (UGSR), to infer essential kinases governing the cellular responses to drug treatments in cancer cells, based on the massively collected drug-kinase interactions and drug sensitivity datasets over hundreds of cancer cell lines. The inferred essential kinases can be subsequently used to calculate the cancer cell sensitivities to combination drugs. Results: The leave-one-out cross validations and two real cases show that our method achieve high performance in predict drug sensitivities of combination drugs. Moreover, a user-friendly web interface with interactive network viewer, tabular viewer and other graphical visualization plugins, has been implemented to facilitate data access and interpretation.
推荐引用方式
GB/T 7714：

Liu Hui,Luo Libo,Cheng Zhanzhan, et al. Group-sparse Modeling Drug-kinase Networks for Predicting Combinatorial Drug Sensitivity in Cancer Cells [J].CURRENT BIOINFORMATICS,2018,13(5):437-443.
APA：

Liu Hui,Luo Libo,Cheng Zhanzhan,Sun Jianjiang,&Zhou Shuigeng.(2018).Group-sparse Modeling Drug-kinase Networks for Predicting Combinatorial Drug Sensitivity in Cancer Cells .CURRENT BIOINFORMATICS,13(5):437-443.
MLA：

Liu Hui, et al. "Group-sparse Modeling Drug-kinase Networks for Predicting Combinatorial Drug Sensitivity in Cancer Cells" .CURRENT BIOINFORMATICS 13,5(2018):437-443.

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Group-sparse Modeling Drug-kinase Networks for Predicting Combinatorial Drug Sensitivity in Cancer Cells 会议论文 期刊论文

Group-sparse Modeling Drug-kinase Networks for Predicting Combinatorial Drug Sensitivity in Cancer Cells 会议论文期刊论文