Systems Toxicology and Systems Pharmacology

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Current Participant: Heeju Noh

Elucidating the mode of action of chemical compounds is of great interest in drug discovery and toxicology. In this regard, advances in high-throughput omics technology have been playing a crucial role in providing the data for elucidating cellular entities which interact with drug and chemical compounds. Cellular-wide response such as whole-genome gene expression profile, to genetic perturbations and chemical compounds can now be measured easily and cheaply. Furthermore, large amount of omics data are available from the ever-growing public biological databases. Because such data are typically of high dimensionality, the use of computational methods has become necessary in their analysis, for example in the inference of gene regulatory networks 

Computational systems biology has provided many tools to analyze omics data for target predictions. Recently, we released a network analyses method, called DeltaNet, for identifying the direct gene targets of compounds. DeltaNet is a robust and numerically efficient tool for predicting genetic perturbations from transcriptional expression profiles. Importantly, unlike existing strategies, DeltaNet does not require any parameter tuning nor data of known perturbations to provide accurate gene target predictions.

Reference

  • Noh, H. and Gunawan, R. Inferring causal gene targets from time course expression data. IFAC-PapersOnLine 49(26): 350-356. abstract
  • Noh, H., and Gunawan, R. Inferring gene targets of drugs and chemical compounds from gene expression profiles. Bioinformatics. 2016. abstract
 
 
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Thu Jun 22 18:47:14 CEST 2017
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