Abstract
Traditional drug discovery practices usually adopt the "one drug - one target" approach, which ignore the fact the disease occurrence is usually the result of an extremely complex combination of molecular events. Pathway-based approaches address this limitation by considering biological pathways as potential drug targets. A first step of pathwaybased drug discovery is to identify associations between drug candidates and biological pathways. This has been made possible by the availability of high-dimensional transcriptional and drug sensitivity profile data. In this chapter, we describe two statistical methods, "iFad" and "iPad", which perform drug-pathway association analysis by integrating these two types high-dimensional data. We also demonstrate their utilities by applying them to the NCI-60 data set.
Original language | English |
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Title of host publication | Integrating Omics Data |
Publisher | Cambridge University Press |
Pages | 425-444 |
Number of pages | 20 |
ISBN (Electronic) | 9781107706484 |
ISBN (Print) | 9781107069114 |
DOIs | |
Publication status | Published - 1 Jan 2015 |
Scopus Subject Areas
- General Medicine