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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21980

Title: A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development
Authors: Perualila-Tan, Nolen
Kasim, Adetayo
Talloen, Willem
Verbist, Bie
Hinrich Ghoelmann
QSTAR Consortium
Shkedy, Ziv
Khamiakova, Tatsiana
Otava, Martin
Issue Date: 2016
Citation: Statistical Applications in Genetics and Molecular Biology, 15 (4), p. 291-304
Abstract: The modern drug discovery process involves multiple sources of high-dimensional data. This imposes the challenge of data integration. A typical example is the integration of chemical structure (fingerprint features), phenotypic bioactivity (bioassay read-outs) data for targets of interest, and transcriptomic (gene expression) data in early drug discovery to better understand the chemical and biological mechanisms of candidate drugs, and to facilitate early detection of safety issues prior to later and expensive phases of drug development cycles. In this paper, we discuss a joint model for the transcriptomic and the phenotypic variables conditioned on the chemical structure. This modeling approach can be used to uncover, for a given set of compounds, the association between gene expression and biological activity taking into account the influence of the chemical structure of the compound on both variables. The model allows to detect genes that are associated with the bioactivity data facilitating the identification of potential genomic biomarkers for compounds efficacy. In addition, the effect of every structural feature on both genes and pIC50 and their associations can be simultaneously investigated. Two oncology projects are used to illustrate the applicability and usefulness of the joint model to integrate multi-source high-dimensional information to aid drug discovery.
URI: http://hdl.handle.net/1942/21980
DOI: 10.1515/sagmb-2014-0086
ISI #: 000380816300002
ISSN: 2194-6302
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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