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Inferring unobserved perturbations from gene expression data

Bioconductor version: Release (3.19)

Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models.

Author: Martin Pirkl [aut, cre]

Maintainer: Martin Pirkl <martinpirkl at>

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biocViews ATACSeq, CRISPR, Classification, DNASeq, DifferentialExpression, DifferentialMethylation, GeneExpression, GeneSignaling, Network, NetworkInference, NeuralNetwork, Pathways, PooledScreens, RNASeq, SingleCell, Software, SystemsBiology
Version 1.12.0
In Bioconductor since BioC 3.13 (R-4.1) (3 years)
License GPL-3
Depends R (>= 4.1), mnem
Imports e1071, nnet, randomForest, naturalsort, graphics, stats, utils, matrixStats, epiNEM
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