Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track
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- Benchmarking blockchain-based gene-drug interaction data sharing methods
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Background: Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. Basic Procedures: We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. Main Findings: We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions. Principal Conclusions: We show the benchmarking results of novel blockchain-based methods for institutions to share patient outcomes related to gene-drug interactions. Our findings support blockchain utilization in healthcare, genomic and biomedical applications. The source code is publicly available at https://github.com/tsungtingkuo/genedrug.
|Tidsskrift||International Journal of Medical Informatics|
|Status||Udgivet - 2021|
The authors would like to acknowledge the support from PlatON International Limited. We would also like to acknowledge Dr. Heidi J. Sofia, Dr. Haixu Tang, Dr. XiaoFeng Wang, Dr. Xiaoqian Jiang, Dr. Arif Harmanci, and Dr. Miran Kim for their support in co-organizing the competition and the workshop. The use of the integrating Data for Analysis, Anonymization, and SHaring (iDASH) 2.0 Amazon Web Services (AWS) cloud network is supported by Dr. Michael Hogarth, Andrew Greaves and Jit Bhattacharya.
The competition is funded by the U.S. National Institutes of Health (NIH) (R13HG009072). T.-T. Kuo is partly funded by the National Human Genome Research Institute (NHGRI) of the U.S. NIH under Award Number R00HG009680, the U.S. NIH (R01HL136835 and R01GM118609), and UCSD Academic Senate Research Grant RG084150. L. Ohno-Machado is funded by the U.S. NIH (R01GM118609, R01HL136835, R01HG011066). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. C. M. Hudson and N. Pattengale are partly supported by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.
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