Artificial intelligence, physiological genomics, and precision medicine
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
ID 4487738
Author(s) Williams, Anna Marie; Liu, Yong; Regner, Kevin R.; Jotterand, Fabrice; Liu, Pengyuan; Liang, Mingyu
Author(s) at UniBasel Jotterand, Fabrice
Year 2018
Title Artificial intelligence, physiological genomics, and precision medicine
Journal Physiological genomics
Volume 50
Number 4
Pages / Article-Number 237-243
Abstract Big data are a major driver in the development of precision medicine. Efficient analysis methods are needed to transform big data into clinically-actionable knowledge. To accomplish this, many researchers are turning toward machine learning (ML), an approach of artificial intelligence (AI) that utilizes modern algorithms to give computers the ability to learn. Much of the effort to advance ML for precision medicine has been focused on the development and implementation of algorithms and the generation of ever larger quantities of genomic sequence data and electronic health records. However, relevance and accuracy of the data are as important as quantity of data in the advancement of ML for precision medicine. For common diseases, physiological genomic readouts in disease-applicable tissues may be an effective surrogate to measure the effect of genetic and environmental factors and their interactions that underlie disease development and progression. Disease-applicable tissue may be difficult to obtain, but there are important exceptions such as kidney needle biopsy specimens. As AI continues to advance, new analytical approaches, including those that go beyond data correlation, need to be developed and ethical issues of AI need to be addressed. Physiological genomic readouts in disease-relevant tissues, combined with advanced AI, can be a powerful approach for precision medicine for common diseases.
Publisher American Physiological Society
ISSN/ISBN 1531-2267 ; 1094-8341
Full Text on edoc No
Digital Object Identifier DOI 10.1152/physiolgenomics.00119.2017
PubMed ID
ISI-Number WOS:000440969300002
Document type (ISI) Journal Article

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