Keywords: Bioactivity, Biomarkers, Chemical Structure, Joint Model, Multi-source
Webpages:
https://cran.r-project.org/web/packages/IntegratedJM/index.html In recent days, data from different sources need to be integrated together in order to arrive at meaningful conclusions. In drug-discovery experiments, most of the different data sources, related to a new set of compounds under development, are of high-dimension. For example, in order to investigate the properties of a new set of compounds, pharmaceutical companies need to analyse chemical structure (fingerprint features) of the compounds, phenotypic bioactivity (bioassay read-outs) data for targets of interest and transcriptomic(gene expression) data. Perualila-Tan et al. (2016) proposed a joint model in which the three data sources are included to better detect the association between gene expression and biological activity. For a given set of compounds, the joint modeling approach accounts for a possible effect of the chemical structure of the compound on both variables. The joint model allows us to identify genes as potential biomarkers for compound’s efficacy. The joint modeling approach, proposed by Perualila-Tan et al. (2016), is implemented in the
IntegratedJM R package which provides, in addition to model estimation and inference, a set of exploratory and visualization functions that can be used to clearly present the results. The joint model and the
IntegratedJM R package are discussed in details in Perualila et al. (2016) as well.
References Perualila, Nolen Joy, Ziv Shkedy, Rudradev Sengupta, Theophile Bigirumurame, Luc Bijnens, Willem Talloen, Bie Verbist, Hinrich W.H. Göohlmann, Adetayo Kasim, and QSTAR Consortium. 2016. “Applied Surrogate Endpoint Evaluation Methods with Sas and R.” In, edited by Ariel Alonso, Theophile Bigirumurame, Tomasz Burzykowski, Marc Buyse, Geert Molenberghs, Leacky Muchene, Nolen Joy Perualila, Ziv Shkedy, and Wim Van der Elst, 275–309. CRC Press.
Perualila-Tan, Nolen, Adetayo Kasim, Willem Talloen, Bie Verbist, Hinrich W.H. Göhlmann, QSTAR Consortium, and Ziv Shkedy. 2016. “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.”
Statistical Applications in Genetics and Molecular Biology 15: 291–304. doi:
10.1515/sagmb-2014-0086.
