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Customized care 2020: how medical sequencing and network biology will enable personalized medicine
Applications of next-generation nucleic acid sequencing technologies will lead to the development of precision diagnostics that will, in turn, be a major technology enabler of precision medicine. Terabyte-scale, multidimensional data sets derived using these technologies will be used to reverse engi...
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Formato: | Texto |
Lenguaje: | English |
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Biology Reports Ltd
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2948270/ https://www.ncbi.nlm.nih.gov/pubmed/20948615 http://dx.doi.org/10.3410/B1-73 |
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author | Boguski, Mark S Arnaout, Ramy Hill, Colin |
author_facet | Boguski, Mark S Arnaout, Ramy Hill, Colin |
author_sort | Boguski, Mark S |
collection | PubMed |
description | Applications of next-generation nucleic acid sequencing technologies will lead to the development of precision diagnostics that will, in turn, be a major technology enabler of precision medicine. Terabyte-scale, multidimensional data sets derived using these technologies will be used to reverse engineer the specific disease networks that underlie individual patients’ conditions. Modeling and simulation of these networks in the presence of virtual drugs, and combinations of drugs, will identify the most efficacious therapy for precision medicine and customized care. In coming years the practice of medicine will routinely employ network biology analytics supported by high-performance supercomputing. |
format | Text |
id | pubmed-2948270 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Biology Reports Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-29482702010-10-14 Customized care 2020: how medical sequencing and network biology will enable personalized medicine Boguski, Mark S Arnaout, Ramy Hill, Colin F1000 Biol Rep Review Article Applications of next-generation nucleic acid sequencing technologies will lead to the development of precision diagnostics that will, in turn, be a major technology enabler of precision medicine. Terabyte-scale, multidimensional data sets derived using these technologies will be used to reverse engineer the specific disease networks that underlie individual patients’ conditions. Modeling and simulation of these networks in the presence of virtual drugs, and combinations of drugs, will identify the most efficacious therapy for precision medicine and customized care. In coming years the practice of medicine will routinely employ network biology analytics supported by high-performance supercomputing. Biology Reports Ltd 2009-09-28 /pmc/articles/PMC2948270/ /pubmed/20948615 http://dx.doi.org/10.3410/B1-73 Text en © 2009 Biology Reports Ltd http://creativecommons.org/licenses/by-nc/3.0/legalcode This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You may not use this work for commercial purposes |
spellingShingle | Review Article Boguski, Mark S Arnaout, Ramy Hill, Colin Customized care 2020: how medical sequencing and network biology will enable personalized medicine |
title | Customized care 2020: how medical sequencing and network biology will enable personalized medicine |
title_full | Customized care 2020: how medical sequencing and network biology will enable personalized medicine |
title_fullStr | Customized care 2020: how medical sequencing and network biology will enable personalized medicine |
title_full_unstemmed | Customized care 2020: how medical sequencing and network biology will enable personalized medicine |
title_short | Customized care 2020: how medical sequencing and network biology will enable personalized medicine |
title_sort | customized care 2020: how medical sequencing and network biology will enable personalized medicine |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2948270/ https://www.ncbi.nlm.nih.gov/pubmed/20948615 http://dx.doi.org/10.3410/B1-73 |
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