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Review
. 2016 Mar;99(3):285-97.
doi: 10.1002/cpt.318.

Leveraging big data to transform target selection and drug discovery

B Chen  1 , A J Butte  1
Affiliations
Review

Leveraging big data to transform target selection and drug discovery

B Chen et al. Clin Pharmacol Ther. 2016 Mar.

Abstract

The advances of genomics, sequencing, and high throughput technologies have led to the creation of large volumes of diverse datasets for drug discovery. Analyzing these datasets to better understand disease and discover new drugs is becoming more common. Recent open data initiatives in basic and clinical research have dramatically increased the types of data available to the public. The past few years have witnessed successful use of big data in many sectors across the whole drug discovery pipeline. In this review, we will highlight the state of the art in leveraging big data to identify new targets, drug indications, and drug response biomarkers in this era of precision medicine.

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Figures

Figure 1
Figure 1
Public datasets can be leveraged to identify new targets, drug indications, and drug response biomarkers.
Figure 2
Figure 2
An illustration of big data approaches to identifying new targets.
Figure 3
Figure 3
An illustration of big data approaches to identifying new drug indications.
Figure 4
Figure 4
An illustration of big data approaches to identifying new drug response biomarkers.
See this image and copyright information in PMC

References

    1. EMBL–European Bioinformatics Institute EMBL‐EBI Annual Scientific Report 2014. <https://www.embl.de/aboutus/communication_outreach/publications/ebi_ar/e...>.
    1. Marx, V. Biology: the big challenges of big data. Nature 498, 255–260 (2013). - PubMed
    1. Barabási, A.L. , Gulbahce, N. & Loscalzo, J. Network medicine: a network‐based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011). - PMC - PubMed
    1. Chen, B. & Butte, A.J. Network medicine in disease analysis and therapeutics. Clin. Pharmacol. Ther. 94, 627–629 (2013). - PMC - PubMed
    1. Mantione, K.J. et al Comparing bioinformatic gene expression profiling methods: microarray and RNA‐Seq. Med. Sci. Monit. Basic Res. 20, 138–142 (2014). - PMC - PubMed

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