Implementation of Kernelized score functions and other semisupervised learning algorithms for node label ranking in biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline networkbased methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel userdefined score functions and kernels.
Package details 


Author  Giorgio Valentini  AnacletoLab, Dipartimento di Informatica, Universita' degli Studi di Milano 
Maintainer  Giorgio Valentini <[email protected]> 
License  GPL (>= 2) 
Version  1.0 
Package repository  View on CRAN 
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