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Trends Pharmacol Sci. 2014 Sep;35(9):450-60. doi: 10.1016/j.tips.2014.07.001. Epub 2014 Aug 7.

Lean Big Data integration in systems biology and systems pharmacology.

Author information

1
Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA. Electronic address: avi.maayan@mssm.edu.
2
Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA.

Abstract

Data sets from recent large-scale projects can be integrated into one unified puzzle that can provide new insights into how drugs and genetic perturbations applied to human cells are linked to whole-organism phenotypes. Data that report how drugs affect the phenotype of human cell lines and how drugs induce changes in gene and protein expression in human cell lines can be combined with knowledge about human disease, side effects induced by drugs, and mouse phenotypes. Such data integration efforts can be achieved through the conversion of data from the various resources into single-node-type networks, gene-set libraries, or multipartite graphs. This approach can lead us to the identification of more relationships between genes, drugs, and phenotypes as well as benchmark computational and experimental methods. Overall, this lean 'Big Data' integration strategy will bring us closer toward the goal of realizing personalized medicine.

KEYWORDS:

data integration; network analysis; network pharmacology; side-effect prediction; systems pharmacology; target prediction

PMID:
25109570
PMCID:
PMC4153537
DOI:
10.1016/j.tips.2014.07.001
[Indexed for MEDLINE]
Free PMC Article

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