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lpNet (version 2.4.0)

Linear Programming Model for Network Inference

Description

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.

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Version

Version

2.4.0

License

Artistic License 2.0

Maintainer

Lars Kaderali

Last Published

February 15th, 2017

Functions in lpNet (2.4.0)

calcRangeLambda

Compute Range Of Penalty Parameter Lambda.
getBaseline

Get Baseline Vector.
summarizeRepl

Summarize Replicate Measurements
calcActivation

Calculate Activation Matrix
generateTimeSeriesNetStates

Generate Time Series Network States
lpNet-package

Network Inference Of Perturbation Data Using a Linear Programming Approach.
doILP

Do The Network Inference With The Linear Programming Approach.
getSampleAdja

Get The Sample Adjacency.
getEdgeAnnot

Get the annotation of the edges.
calcPrediction

Calculate Predicted Observation.
CV

Cross-validation
getAdja

Get Adjacency Matrix.
getObsMat

Get Observation Matrix.