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Network-Driven Analysis Methods and their Application to Drug Discovery

Network-Driven Analysis Methods and their Application to Drug Discovery

Daniel Ziemek, Christoph Brockel
Copyright: © 2011 |Pages: 22
ISBN13: 9781609604912|ISBN10: 1609604911|EISBN13: 9781609604929
DOI: 10.4018/978-1-60960-491-2.ch013
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MLA

Ziemek, Daniel, and Christoph Brockel. "Network-Driven Analysis Methods and their Application to Drug Discovery." Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., IGI Global, 2011, pp. 294-315. https://doi.org/10.4018/978-1-60960-491-2.ch013

APA

Ziemek, D. & Brockel, C. (2011). Network-Driven Analysis Methods and their Application to Drug Discovery. In L. Liu, D. Wei, Y. Li, & H. Lei (Eds.), Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications (pp. 294-315). IGI Global. https://doi.org/10.4018/978-1-60960-491-2.ch013

Chicago

Ziemek, Daniel, and Christoph Brockel. "Network-Driven Analysis Methods and their Application to Drug Discovery." In Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., 294-315. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-491-2.ch013

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Abstract

Drug discovery and development face tremendous challenges to find promising intervention points for important diseases. Any therapeutic agent targeting such an intervention point must prove its efficacy and safety in patients. Success rates measured from first studies in human to registration average around 10% only. Over the last decade, massive knowledge on biological systems has been accumulated and genome-scale primary data are produced at an ever increasing rate. In parallel, methods to use that knowledge have matured. This chapter will present some of the problems facing the pharmaceutical industry and elaborate on the current state of network-driven analysis methods. It will focus especially on semi-quantitative methods that are applicable to large-scale data analysis and point out their potential use in many relevant drug discovery challenges.

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