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转化生物信息学方法在药理学研究中的潜力。

The potential of translational bioinformatics approaches for pharmacology research.

作者信息

Li Lang

机构信息

Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN.

Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN.

出版信息

Br J Clin Pharmacol. 2015 Oct;80(4):862-7. doi: 10.1111/bcp.12622. Epub 2015 Jun 1.

Abstract

The field of bioinformatics has allowed the interpretation of massive amounts of biological data, ushering in the era of 'omics' to biomedical research. Its potential impact on pharmacology research is enormous and it has shown some emerging successes. A full realization of this potential, however, requires standardized data annotation for large health record databases and molecular data resources. Improved standardization will further stimulate the development of system pharmacology models, using translational bioinformatics methods. This new translational bioinformatics paradigm is highly complementary to current pharmacological research fields, such as personalized medicine, pharmacoepidemiology and drug discovery. In this review, I illustrate the application of transformational bioinformatics to research in numerous pharmacology subdisciplines.

摘要

生物信息学领域使得人们能够解读海量的生物数据,开创了生物医学研究的“组学”时代。它对药理学研究的潜在影响巨大,且已取得了一些初步成功。然而,要充分实现这一潜力,需要对大型健康记录数据库和分子数据资源进行标准化数据注释。通过转化生物信息学方法,进一步改进标准化将推动系统药理学模型的发展。这种新的转化生物信息学范式与当前的药理学研究领域,如个性化医学、药物流行病学和药物发现,具有高度互补性。在本综述中,我阐述了转化生物信息学在众多药理学子学科研究中的应用。

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