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Opportunities for Computational Techniques for Multi-Omics Integrated Personalized Medicine
Kebin Jia(
),
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Zhang Y, Cheng Y, Jia K, et al.
Opportunities for Computational Techniques for Multi-Omics Integrated Personalized Medicine.
Tsinghua Science and Technology,
2014, 19(6): 545-558.
https://doi.org/10.1109/TST.2014.6961025
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Personalized medicine is defined as "a model of healthcare that is predictive, personalized, preventive, and participator" and has very broad content. With the rapid development of high-throughput technologies, an explosive accumulation of biological information is collected from multiple layers of biological processes, including genomics, transcriptomics, proteomics, metabonomics, and interactomics (omics). Implementing integrative analysis of these multiple omics data is the best way of deriving systematical and comprehensive views of living organisms, achieving better understanding of disease mechanisms, and finding operable personalized health treatments. With the help of computational methods, research in the field of biology and biomedicine has gained tremendous benefits over the past few decades. In the new era of personalized medicine, we will rely more on the assistance of computational analysis. In this paper, we briefly review the generation of multiple omics and their basic characteristics. And then the challenges and opportunities for computational analysis are discussed and some state-of-art analysis methods that were recently proposed by peers for integrative analysis of multiple omics data are reviewed. We foresee that further integrated omics data platform and computational tools would help to translate the biological knowledge to clinical usage and accelerate development of personalized medicine.
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Opportunities for Computational Techniques for Multi-Omics Integrated Personalized Medicine
Kebin Jia(
),
),
Abstract
Personalized medicine is defined as "a model of healthcare that is predictive, personalized, preventive, and participator" and has very broad content. With the rapid development of high-throughput technologies, an explosive accumulation of biological information is collected from multiple layers of biological processes, including genomics, transcriptomics, proteomics, metabonomics, and interactomics (omics). Implementing integrative analysis of these multiple omics data is the best way of deriving systematical and comprehensive views of living organisms, achieving better understanding of disease mechanisms, and finding operable personalized health treatments. With the help of computational methods, research in the field of biology and biomedicine has gained tremendous benefits over the past few decades. In the new era of personalized medicine, we will rely more on the assistance of computational analysis. In this paper, we briefly review the generation of multiple omics and their basic characteristics. And then the challenges and opportunities for computational analysis are discussed and some state-of-art analysis methods that were recently proposed by peers for integrative analysis of multiple omics data are reviewed. We foresee that further integrated omics data platform and computational tools would help to translate the biological knowledge to clinical usage and accelerate development of personalized medicine.
Keywords:
personalized medicine, translational bioinformatics, multi-omics integration
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Received: 09 June 2014
Accepted: 16 June 2014
Published:
20 November 2014
Issue date: December 2014
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Acknowledgements
This paper was supported by the Project for the Innovation Team of Beijing, the National Natural Science Foundation of China (No. 81370038), the Beijing Natural Science Foundation (No. 7142012), the Science and Technology Project of Beijing Municipal Education Commission (No. km201410005003), the Rixin Fund of Beijing University of Technology (No. 2013-RX-L04) and the Basic Research Fund of Beijing University of Technology. The authors declare that they have no competing interests.
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