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- W3099320515 endingPage "20170350" @default.
- W3099320515 startingPage "20170350" @default.
- W3099320515 abstract "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’." @default.
- W3099320515 created "2020-11-23" @default.
- W3099320515 creator A5054755903 @default.
- W3099320515 date "2018-08-06" @default.
- W3099320515 modified "2023-10-13" @default.
- W3099320515 title "Machine learning and genomics: precision medicine versus patient privacy" @default.
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- W3099320515 doi "https://doi.org/10.1098/rsta.2017.0350" @default.
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