Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

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Abstract

Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism.

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Christadore, L. M., Pham, L., Kolaczyk, E. D., & Schaus, S. E. (2014). Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets. BMC Systems Biology, 8(1). https://doi.org/10.1186/1752-0509-8-7

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