Comprehensive pharmacogenomic pathway screening by data assimilation

Authors:
Takanori Hasegawa;Rui Yamaguchi;Masao Nagasaki;Seiya Imoto;Satoru Miyano
Affiliations:
Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan;Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan;Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan;Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan;Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
Venue:
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Year:
2011

Citing 2
Cited 0

Bayesian learning of biological pathways on genomic data assimilation

Bioinformatics
DA 1.0

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a computational method to comprehensively screen for pharmacogenomic pathway simulation models. A systematic model generation strategy is developed; candidate pharmacogenomic models are automatically generated from some prototype models constructed from existing literature. The parameters in the model are automatically estimated based on time-course observed gene expression data by data assimilation technique. The candidate simulation models are also ranked based on their prediction power measured by Bayesian information criterion. We generated 53 pharmacogenomic simulation models from five prototypes and applied the proposed method to microarray gene expression data of rat liver cells treated with corticosteroid. We found that some extended simulation models have higher prediction power for some genes than the original models.