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dc.contributor.authorZhang, Jintao
dc.contributor.authorLushington, Gerald H.
dc.contributor.authorHuan, Jun
dc.date.accessioned2014-01-24T22:38:00Z
dc.date.available2014-01-24T22:38:00Z
dc.date.issued2011-07-27
dc.identifier.citationZhang, Jintao, Gerald H Lushington, and Jun Huan. 2011. “The BioAssay Network and Its Implications to Future Therapeutic Discovery.” BMC Bioinformatics 12 Suppl 5 (Suppl 5): S1. http://dx.doi.org/10.1186/1471-2105-12-S5-S1.
dc.identifier.urihttp://hdl.handle.net/1808/12856
dc.description.abstractBackground: Despite intense investment growth and technology development, there is an observed bottleneck in drug discovery and development over the past decade. NIH started the Molecular Libraries Initiative (MLI) in 2003 to enlarge the pool for potential drug targets, especially from the “undruggable” part of human genome, and potential drug candidates from much broader types of drug-like small molecules. All results are being made publicly available in a web portal called PubChem. Results: In this paper we construct a network from bioassay data in PubChem, apply network biology concepts to characterize this bioassay network, integrate information from multiple biological databases (e.g. DrugBank, OMIM, and UniHI), and systematically analyze the potential of bioassay targets being new drug targets in the context of complex biological networks. We propose a model to quantitatively prioritize this druggability of bioassay targets, and literature evidence was found to confirm our prioritization of bioassay targets at a roughly 70% accuracy. Conclusions: Our analysis provide some measures of the value of the MLI data as a resource for both basic chemical biology research and future therapeutic discovery.
dc.publisherBioMed Central
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.titleThe BioAssay network and its implications to future therapeutic discovery
dc.typeArticle
kusw.kuauthorZhang, Jintao
kusw.kuauthorLushington, Gerald H.
kusw.kuauthorHuan, Jun
kusw.kudepartmentBioinformatics
kusw.kudepartmentElectrical Engineering & Computer Science
kusw.oastatusfullparticipation
dc.identifier.doi10.1186/1471-2105-12-S5-S1
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.