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Table 1 Methods for network inference

From: Gene regulatory networks in plants: learning causality from time and perturbation

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]

  1. 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.