I was reading a paper related to bioinformatics where it uses the drug response on the cancer cells and the gene expression of the individual cells are studied to find any useful insights. Specially, using the gene expression of the cells a predictor of the drug response is created.
They have stated that just using the correlation between the gene expression and the drug response might not be a good predictor. But the genes interact through signaling pathways to drive a particular drug response.
What these guys have done is like used PCA on the gene expressions of the cancer cells to use the components which preserve the greatest variance.
Actually, I didn't get what they mean by probe-by-background interaction and how it is calculated.
Can anyone please explain. I googled for a while but didn't get it.
Here are some quotes from the paper where the term is used
Towards this end, we have compared how well drug response can be predicted by simple statistical models, which either directly relate probe and background networks to drug response or consider probe-by-background network interactions.
To generalize this approach, the term ‘probe’ could be replaced by individual transcript expression levels measured through other gene expression methods. Similarly, ‘background networks’ and principal components are used interchangeably. Generally, ‘background networks’ could be represented by any data reduction method that summarizes the expression of a gene network. We demonstrate that probe-by-background network interactions significantly enhance drug response predictions, over and above the predictive power garnered through utilizing individual probes and background networks alone.
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