adj.get.hops

 adjacency matrix; parents in rows, children in columns 

adjmat


This function uses a depth-first search algorithm to identify all the children (and their corresponding depth) of a node.


This package contains a set of functions related to
network inference combining genomic data and prior information
extracted from biomedical literature and structured biological
databases. The main function is able to generate networks using
Bayesian or regression-based inference methods; while the
former is limited to < 100 of variables, the latter may infer
networks with hundreds of variables. Several statistics at the
edge and node levels have been implemented (edge stability,
predictive ability of each node, ...) in order to help the user
to focus on high quality subnetworks. Ultimately, this package
is used in the 'Predictive Networks' web application developed
by the Dana-Farber Cancer Institute in collaboration with
Entagen.

Benjamin HaibeKains

predictionet

Inference for predictive networks designed for (but not limited
to) genomic data

adj.get.hops function

adjacency matrix; parents in rows, children in columns

This function uses a depth-first search algorithm to identify all the children (and their corresponding depth) of a node.

adj.get.hops: Function to identify all children of a parent

Description

Usage

Arguments

Value

Details

Examples