DSD is a program that calculates diffusion state distances between nodes in a graph. It makes use of global topologies via random walks and captures proximity between nodes in terms of node properties such as protein functions among protein-protein interaction graph.
cDSD extends this by including information about interaction confidence
caDSD is build on top of cDSD, and augments it with high-confidence signaling pathway data
capDSD extends caDSD by making use of the topological properties of these signaling pathways
If you use capDSD, please cite:
M. Cao, C. M. Pietras, X. Feng, K. J. Doroschak, T. Schaffner, J. Park, H. Zhang, L. J. Cowen and B. Hescott, New directions for diffusion-based network prediction of protein function: incorporating pathways with confidence Bioinformatics, Volume 30, ISMB 2014 Proceedings, i219-i227, 2014.
A small example ppi and set of pathways is available here
Readme (Also included in the source archive)
Raw data used in the paper are as follows:
Intermediate data used in the paper are as follows:
DSD matrices (Warning: Large)