Large scale statistical inference of signaling pathways from RNAi and microarray data
TL;DR: This paper introduces a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead, and proposes methods to scale up the original approach, which is limited to around 5 genes, to large scale networks.
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Abstract: Background
The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway.
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Citations
Reconstructing evolving signalling networks by hidden Markov nested effects models
TL;DR: Hidden Markov nested effects models (HM-NEMs) are proposed to model the evolving network by a Markov chain on a state space of signalling networks, which are derived from nested effect models (NEMS) of indirect perturbation data.
An integrated microfluidic system capable of sample pretreatment and hybridization for microarrays
TL;DR: A new microfluidic system capable of automatically performing the sample pretreatment and hybridization processes for microarrays is reported, which may provide a useful platform for subsequent genetic analysis and diagnostic applications.
NEMix: Single-cell Nested Effects Models for Probabilistic Pathway Stimulation
Juliane Siebourg-Polster,Daria Mudrak,Mario Emmenlauer,Pauli Rämö,Christoph Dehio,Urs F. Greber,Holger Fröhlich,Niko Beerenwinkel +7 more
TL;DR: In an extensive simulation study, it is shown that the inferred NEMix network has high accuracy and outperforms the classical nested effects model without hidden pathway activity, and improves learning of pathway structures over classical NEMs significantly in the presence of hidden pathway stimulation.
A logic-based method to build signaling networks and propose experimental plans.
Adrien Rougny,Pauline Gloaguen,Nathalie Langonné,Eric Reiter,Pascale Crépieux,Anne Poupon,Christine Froidevaux +6 more
TL;DR: A logic-based method that allows building large signaling networks automatically and leads to a new hypothesis on the activation of MEK by p38MAPK, which is validated experimentally and represents a first step in the demonstration of a cross-talk between these two major MAP kinases pathways.
Single cell network analysis with a mixture of Nested Effects Models.
Martin Pirkl,Martin Pirkl,Niko Beerenwinkel,Niko Beerenwinkel +3 more
- 01 Sep 2018
TL;DR: A mixture of Nested Effects Models (M&NEM) for single‐cell data is developed to simultaneously identify different cellular subpopulations and their corresponding causal networks to explain the heterogeneity in a cell population.
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