Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Data perturbation for escaping local maxima in learning
Eighteenth national conference on Artificial intelligence
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Dynamic deterministic effects propagation networks
Bioinformatics
Fast and efficient dynamic nested effects models
Bioinformatics
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Our current understanding of cellular networks is rather incomplete. We miss important but sofar unknown genes and mechanisms in the pathways. Moreover, we often only have a partial account of the molecular interactions and modifications of the known players. When analyzing the cell, we look through narrow windows leaving potentially important events in blind spots. Network reconstruction is naturally confined to what we have observed. Little is known on how the incompleteness of our observations confounds our interpretation of the available data. Here we ask the question, which features of a network can be confounded by incomplete observations and which cannot. In the context of nested effects models, we show that in the presence of missing observations or hidden factors a reliable reconstruction of the full network is not feasible. Nevertheless, we can show that certain characteristics of signaling networks like the existence of cross talk between certain branches of the network can be inferred in a non-confoundable way. We derive a test for inferring such non-confoundable characteristics of signaling networks. Next, we introduce a new data structure to represent partially reconstructed signaling networks. Finally, we evaluate our method both on simulated data and in the context of a study on early stem cell differentiation in mice.