MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 75006 Paris, France
Institut Curie, PSL Research University, 75005 Paris, France.
Philos Trans A Math Phys Eng Sci. 2018 Sep 13;376(2128). doi: 10.1098/rsta.2017.0350.
Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues. We review how breaches in patient privacy can occur, present recent developments in computational data protection and discuss how they can be combined with legal and ethical perspectives to provide secure frameworks for genomic data sharing.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.
机器学习在计算生物学应用中具有重大的社会影响。特别是，它在精准医疗的发展中发挥了核心作用，即根据患者的临床或遗传特征量身定制治疗方案。然而，这些进展需要研究人员收集和共享大量的基因组数据，这引发了人们对隐私的极大关注。研究人员、研究参与者和管理机构应该意识到参与者的隐私可能受到损害的方式，以及大量关于解决这些问题的技术解决方案的研究。我们回顾了患者隐私可能被侵犯的方式，介绍了计算数据保护的最新进展，并讨论了如何将其与法律和伦理观点相结合，为基因组数据共享提供安全框架。本文是“算法在社会中的日益普及：影响、影响和创新”讨论会议议题的一部分。
