Q:

Currently, I’m working on reconstructing gene regulatory network. It’s

really an interesting topic and I would like to estimate which tools is

suitable for our experimental data. I have read your published paper

"Improved Reconstruction of In Silico Gene Regulatory Networks by

Integrating Knockout and Perturbation

Data". In this paper, I can’t understand the section of learning noise from

deletion data.

Step 1: Calculate the probability of regulation Pb->a for each pair of genes

(b,a). I want to know how to calculate this probability, and this value of

probability can decide potential regulation or not?

I want to ask you that how to work in this section, and I’m appreciated if

you can help me to figure out.

A: Basically we used the expression levels currently believed to be

unaffected by a deletion to form a Gaussian background. Then if a gene

has an expression level far away from the mean of this Gaussian

distribution (by calculating the probability that the expression is as

extreme or more extreme than the observed one based on the Gaussian), we

consider the gene to be affected by the deletion.

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