Table 1.

Methods for network inference

Methods	Information richness	Scalability	References
Correlation/mutual information	Low	High (thousands of genes)	[20,28]
Partial correlation	Medium	Medium (up to 100 genes using heuristics)	[29,33]
Differential equations	Medium	Medium	[2,32,34,36]
Linear regression	Medium	Medium	[38]
Non-linear regression	High	Low (up to 25 genes)	[38]
Boolean	High	Low (up to 25 genes)	[11,35]

It is clear that there is a trade-off between information richness (the number of factors that can be applied to predict gene expression) and the size of the analyzed network. Small networks can be handled by methods that are highly complex and information rich (many linear and non-linear factors can influence a gene within the method). Combining several small network modules holds the potential to analyze a large network [5], although this might not always work.