|The COSNet R package
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Main functionalities of COSNet
is a package which implements the homonym algorithm to infer node
labels in biological networks [reference 1, 2 of the on-line section Overview of the COSNet R Package].
is specifically designed for problems characterized by unbalanced
labellings, that is when negative examples largely outnumber those
- Its setting is semi-supervised: it requires positive and negative instances for the class being predicted (training set), and the unlabelled instances (test set), for which the algorithm infers a label (positive or negative) with regard to the current class.
- The main steps of COSNet are summarized in the following.
Data processing. The package provides functions to partition input data in folds (find.division.strat and find.division.not.strat), and to generate temporary labels for the unlabelled instances (generate_labels), to be used in the learning phase. See Section 5.1.1 of reference  for details about this step.
Learning of parameters. This part of the package realizes the learning procedure of the COSNet algorithm (see Section 5.2 of ). The function generate_points projects nodes in the training set into labelled points in the plane (Section 5.2.1), whereas functions optimizep and optimize_pos_above learn the optimal straight line (Section 5.2.2).
Network dynamics and regularization. Finally, the package provides the function runSubnet
to realize the dynamics (with the learned parameters) of the
sub-network restricted to unlabelled nodes (Section 5.3 of ).
Moreover, the function reg_data allows to simulate a regularized dynamics (Section 5.6 of paper ).
- The function COSNet executes all these steps in the mentioned order.
- Finally, the function cosnet.cross.validation
allows to perform an entire cross validation procedure, with a desired
number of folds, to estimate to generalization capabilities of the
- For details about the single functions of the package, please refer to the Reference Manual.