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.
|Author||Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali|
|Maintainer||Lars Kaderali <[email protected]>|
|License||Artistic License 2.0|
|Package repository||View on GitHub|
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