getEdgeAnnot


Logical: should all edges be positive? Corresponds to learning only activating edges. Default: FALSE.


allpos


The function returns the annotation of the edges needed for the LP. Positive edges are annotated with "w+" and negative with "w-". The given nodes are just enumerated from 1 to n and the edge between node i and j is given by "w+_i_j" for the positive, respectively by "w-_i_j" for the negative edges. The annotation "w_i_^_0" defines the baseline activity of gene i.


annotation

lpNet aims at infering biological networks, in particular signaling and gene networks.
For that it takes perturbation data, either steady-state or time-series,
as input and generates an LP model which allows the inference of signaling networks.
For parameter identification either leave-one-out cross-validation or
stratified n-fold cross-validation can be used.

Lars Kaderali

lpNet

Linear Programming Model for Network Inference

getEdgeAnnot function

Logical: should all edges be positive? Corresponds to learning only activating edges. Default: FALSE.

The function returns the annotation of the edges needed for the LP. Positive edges are annotated with "w+" and negative with "w-". The given nodes are just enumerated from 1 to n and the edge between node i and j is given by "w+_i_j" for the positive, respectively by "w-_i_j" for the negative edges. The annotation "w_i_^_0" defines the baseline activity of gene i.

getEdgeAnnot: Get the annotation of the edges.

Description

Usage

Arguments

Examples