气候变化领域集成服务门户(CCPortal): DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration

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DOI	10.1093/bib/bbab048
	DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration
	Wang, Yongcui; Yang, Yingxi; Chen, Shilong; Wang, Jiguang
通讯作者	Wang, YC (通讯作者)，Chinese Acad Sci, Key Lab Adaptat & Evolut Plateau Biota, Northwest Inst Plateau Biol, Xining 810008, Qinghai, Peoples R China. ; Wang, JG (通讯作者)，Hong Kong Univ Sci & Technol, Div Life Sci, Clear Water Bay, Kowloon, Hong Kong, Peoples R China. ; Wang, JG (通讯作者)，Hong Kong Univ Sci & Technol, Dept Chem & Biol Engn, Clear Water Bay, Kowloon, Hong Kong, Peoples R China.
发表日期	2021
ISSN	1467-5463
EISSN	1477-4054
卷号	22 期号:5
英文摘要	Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering clinical translation, lacking effective methods for multimodal and multisource data integration is becoming a bottleneck. Here we proposed DeepDRK, a machine learning framework for deciphering drug response through kernel-based data integration. To transfer information among different drugs and cancer types, we trained deep neural networks on more than 20 000 pan-cancer cell line-anticancer drug pairs. These pairs were characterized by kernel-based similarity matrices integrating multisource and multi-omics data including genomics, transcriptomics, epigenomics, chemical properties of compounds and known drug-target interactions. Applied to benchmark cancer cell line datasets, our model surpassed previous approaches with higher accuracy and better robustness. Then we applied our model on newly established patient-derived cancer cell lines and achieved satisfactory performance with AUC of 0.84 and AUPRC of 0.77. Moreover, DeepDRK was used to predict clinical response of cancer patients. Notably, the prediction of DeepDRK correlated well with clinical outcome of patients and revealed multiple drug repurposing candidates. In sum, DeepDRK provided a computational method to predict drug response of cancer cells from integrating pharmacogenomic datasets, offering an alternative way to prioritize repurposing drugs in precision cancer treatment. The DeepDRK is freely available via https://github.com/wangyc82/DeepDRK.
关键词	NEURAL-NETWORKS SENSITIVITY PREDICTION IDENTIFICATION HETEROGENEITY ARCHITECTURES MECHANISM DISCOVERY RESOURCE EVALUATE
英文关键词	drug repurposing; multi-omics data sources; kernel-based data integration; machine learning; cancer precision medicine
语种	英语
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS记录号	WOS:000709461800081
来源期刊	BRIEFINGS IN BIOINFORMATICS
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254693
作者单位	[Wang, Yongcui] Chinese Acad Sci, Key Lab Adaptat & Evolut Plateau Biota, Northwest Inst Plateau Biol, Xining 810008, Qinghai, Peoples R China; [Yang, Yingxi; Wang, Jiguang] Hong Kong Univ Sci & Technol, Dept Chem & Biol Engn, Clear Water Bay, Kowloon, Hong Kong, Peoples R China; [Chen, Shilong] Chinese Acad Sci, Key Lab Adaptat & Evolut Plateau Biota, Inst Sanjiangyuan Natl Pk, Xining, Peoples R China; [Wang, Jiguang] Hong Kong Univ Sci & Technol, Dept Chem & Biol Engn, Div Life Sci, Hong Kong, Peoples R China; [Wang, Jiguang] Hong Kong Univ Sci & Technol, State Key Lab Mol Neurosci, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yongcui,Yang, Yingxi,Chen, Shilong,et al. DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration[J]. 中国科学院西北生态环境资源研究院,2021,22(5).
APA	Wang, Yongcui,Yang, Yingxi,Chen, Shilong,&Wang, Jiguang.(2021).DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.BRIEFINGS IN BIOINFORMATICS,22(5).
MLA	Wang, Yongcui,et al."DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration".BRIEFINGS IN BIOINFORMATICS 22.5(2021).