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Implementing a universal informed consent process for the Research Program.为该研究项目实施通用的知情同意程序。
Pac Symp Biocomput. 2019;24:427-438.
2
Protecting Genomic Data Privacy with Probabilistic Modeling.用概率模型保护基因组数据隐私
Pac Symp Biocomput. 2019;24:403-414.
3
Leveraging summary statistics to make inferences about complex phenotypes in large biobanks.利用汇总统计数据对大型生物样本库中的复杂表型进行推断。
Pac Symp Biocomput. 2019;24:391-402.
4
Evaluation of patient re-identification using laboratory test orders and mitigation via latent space variables.使用实验室检查医嘱评估患者重新识别情况及通过潜在空间变量进行缓解
Pac Symp Biocomput. 2019;24:415-426.
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Statistical Detection of Relatives Typed with Disjoint Forensic and Biomedical Loci.基于不相关法医和生物医学位点的亲属关联的统计检测。
Cell. 2018 Oct 18;175(3):848-858.e6. doi: 10.1016/j.cell.2018.09.008. Epub 2018 Oct 11.
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Identity inference of genomic data using long-range familial searches.利用远程家族搜索推断基因组数据的身份信息。
Science. 2018 Nov 9;362(6415):690-694. doi: 10.1126/science.aau4832. Epub 2018 Oct 11.
7
Analysis of sensitive information leakage in functional genomics signal profiles through genomic deletions.通过基因组缺失分析功能基因组信号谱中的敏感信息泄露。
Nat Commun. 2018 Jun 22;9(1):2453. doi: 10.1038/s41467-018-04875-5.
8
Comparative Approaches to Genetic Discrimination: Chasing Shadows?比较法视角下的基因歧视问题研究:是在捕风捉影吗?
Trends Genet. 2017 May;33(5):299-302. doi: 10.1016/j.tig.2017.02.002. Epub 2017 Mar 30.
9
Are Data Sharing and Privacy Protection Mutually Exclusive?数据共享和隐私保护是否相互排斥?
Cell. 2016 Nov 17;167(5):1150-1154. doi: 10.1016/j.cell.2016.11.004.
10
Quantification of private information leakage from phenotype-genotype data: linking attacks.从表型-基因型数据中量化隐私信息泄露:链接攻击
Nat Methods. 2016 Mar;13(3):251-6. doi: 10.1038/nmeth.3746. Epub 2016 Feb 1.

当生物学涉及个人隐私:生物大数据中隐私与伦理的潜在挑战。

When Biology Gets Personal: Hidden Challenges of Privacy and Ethics in Biological Big Data.

作者信息

Gürsoy Gamze, Harmanci Arif, Tang Haixu, Ayday Erman, Brenner Steven E

机构信息

Computational Biology and Bioinformatics Program, Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, 06511, USA*This work is partially supported by NIH grant U01EB023686.,

出版信息

Pac Symp Biocomput. 2019;24:386-390.

PMID:30864339
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7577606/
Abstract

High-throughput technologies for biological data acquisition are advancing at an increasing pace. Most prominently, the decreasing cost of DNA sequencing has led to an exponential growth of sequence information, including individual human genomes. This session of the 2019 Pacific Symposium on Biocomputing presents the distinctive privacy and ethical challenges related to the generation, storage, processing, study, and sharing of individuals' biological data generated by multitude of technologies including but not limited to genomics, proteomics, metagenomics, bioimaging, biosensors, and personal health trackers. The mission is to bring together computational biologists, experimental biologists, computer scientists, ethicists, and policy and lawmakers to share ideas, discuss the challenges related to biological data and privacy.

摘要

用于生物数据采集的高通量技术正以越来越快的速度发展。最显著的是,DNA测序成本的降低导致了序列信息呈指数级增长,包括个人人类基因组。2019年太平洋生物计算研讨会上的这一环节介绍了与通过多种技术(包括但不限于基因组学、蛋白质组学、宏基因组学、生物成像、生物传感器和个人健康追踪器)生成、存储、处理、研究和共享个人生物数据相关的独特隐私和伦理挑战。其使命是召集计算生物学家、实验生物学家、计算机科学家、伦理学家以及政策制定者和立法者,以分享想法,讨论与生物数据和隐私相关的挑战。