A Method for 'Connecting The Dots' in Weighted Graphs
Combine datasets
Fisher's Combined P-value
Impute missing values
Generate surrogate profiles
Z-transform available data
Connect a node to its unvisited "extended" neighbors
Diffuse Probability P1 from a starting node
Capture the current state of probability diffusion
Network pruning for disease-specific network determination
Capture the current location of a network walker
Minimum encoding length
Get minimum patient distances
Generate patient-specific bitstrings
CTDncd: A network-based distance metric.
Generate multi-node node rankings ("adaptive" walk)
Generate single-node node rankings ("fixed" walk)
Entropy of a bit-string
DirSim: The Jaccard distance with directionality incorporated.
A method for pattern discovery in weighted graphs as outlined in Thistlethwaite et al. (2021) <doi:10.1371/journal.pcbi.1008550>. Two use cases are achieved: 1) Given a weighted graph and a subset of its nodes, do the nodes show significant connectedness? 2) Given a weighted graph and two subsets of its nodes, are the subsets close neighbors or distant?