NetInt: Methods for Unweighted and Weighted Network Integration
Implementation of network integration approaches
comprising unweighted and weighted integration methods. Unweighted integration
is performed considering the average, per-edge average, maximum and minimum of
networks edges. Weighted integration takes into account a weight for each
network during the fusion process, where the weights express
the ”predictiveness strength” of each network considering a specific predictive
task. Weights can be learned using a machine learning algorithm able to associate
the weights to the assessment of the accuracy of the learning algorithm
trained on the network itself. The implemented methods can be applied to
effectively integrate different biological networks modelling a wide range
of problems in bioinformatics (e.g. disease gene prioritization, protein
function prediction, drug repurposing, clinical outcome prediction).
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