Description Details Author(s) References
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.
Package: | lpNet |
Type: | Package |
Version: | 1.99.2 |
Date: | 2013-01-11 |
License: | Artistic License 2.0 |
B. Knapp, M. R. A. Matos, J. Mazur, L. Kaderali
Maintainer: bettina.knapp@helmholtz-muenchen.de
Bettina Knapp and Lars Kaderali, Reconstruction of cellular signal transduction networks using perturbation assays and linear programming, PLoS ONE, 2013.
Marta R. A. Matos, Network inference : extension of linear programming model for time-series data, Master's thesis, Department of Informatics, University of Minho.
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