lpNet-package

lpNet


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.


linear programming approach

 network inference 

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

Linear Programming Model for Network Inference

lpNet-package function

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.

lpNet-package: Network Inference Of Perturbation Data Using a Linear Programming Approach.

Description

Arguments

Details

References