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In regression-based methods for network inference, we infer regulators (parent nodes) for each target gene.
We discuss existing techniques for network inference.
Another useful approach for network inference is Bayesian network computational methods [48].
As it will be shown later, this is essential for network inference when applying IT methods.
We review and compare recent computational technologies for network inference applied to drug discovery.
The material presented here is intended to show some useful features of the application of IT methods for network inference and assessment.
Afterwards, P-CENI obtain the embedded node representations in a space of the critical dimension and then apply a clustering algorithm to find the clusters for network inference.
Actually, several existing methods for network inference can be interpreted as special instances of this framework.
Evaluation and comparison of the performance of algorithms for network inference and data prediction is still an open issue.
Identifying the direction of an interaction is an important step in reverse network engineering and requires larger datasets for network inference [17].
This is why null-mutant z-score was an efficacious signal for network inference, but measures of statistical dependence were not.
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