Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze  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 network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.

Fast Nearest Neighbour Search (wraps Arya and Mount's ANN
library)
Sunil Arya and David Mount (for ANN), Samuel E. Kemp, Gregory Jefferis
ANN library is copyright University of Maryland and Sunil
Arya and David Mount. See file COPYRIGHT for details.
Finds the k nearest neighbours for every point in a given dataset
in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
support for approximate as well as exact searches, fixed radius searches
and bd as well as kd trees.

Fast Nearest Neighbour Search (Wraps ANN Library) Using L2
Metric
Finds the k nearest neighbours for every point in a given dataset
in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
support for approximate as well as exact searches, fixed radius searches
and 'bd' as well as 'kd' trees. The distance is computed using the L2
(Euclidean) metric. Please see package 'RANN.L1' for the same
functionality using the L1 (Manhattan, taxicab) metric.

Fast Nearest Neighbour Search (Wraps Arya and Mount's ANN
Library)

Fast Nearest Neighbour Search (Wraps ANN Library) Using L1
Metric
Sunil Arya and David Mount (for ANN), Samuel E. Kemp, Gregory Jefferis,
Kirill Müller
Finds the k nearest neighbours for every point in a given dataset
in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
support for approximate as well as exact searches, fixed radius searches
and 'bd' as well as 'kd' trees. The distance is computed using the L1
(Manhattan, taxicab) metric. Please see package 'RANN' for the same
functionality using the L2 (Euclidean) metric.
Orphaned on 2018-08-10 as C++ issues were not corrected
despite reminders.