4,531 research outputs found
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
Reconstructing transcriptional regulatory networks is an important task in
functional genomics. Data obtained from experiments that perturb genes by
knockouts or RNA interference contain useful information for addressing this
reconstruction problem. However, such data can be limited in size and/or are
expensive to acquire. On the other hand, observational data of the organism in
steady state (e.g. wild-type) are more readily available, but their
informational content is inadequate for the task at hand. We develop a
computational approach to appropriately utilize both data sources for
estimating a regulatory network. The proposed approach is based on a three-step
algorithm to estimate the underlying directed but cyclic network, that uses as
input both perturbation screens and steady state gene expression data. In the
first step, the algorithm determines causal orderings of the genes that are
consistent with the perturbation data, by combining an exhaustive search method
with a fast heuristic that in turn couples a Monte Carlo technique with a fast
search algorithm. In the second step, for each obtained causal ordering, a
regulatory network is estimated using a penalized likelihood based method,
while in the third step a consensus network is constructed from the highest
scored ones. Extensive computational experiments show that the algorithm
performs well in reconstructing the underlying network and clearly outperforms
competing approaches that rely only on a single data source. Further, it is
established that the algorithm produces a consistent estimate of the regulatory
network.Comment: 24 pages, 4 figures, 6 table
Identifiability and transportability in dynamic causal networks
In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks.
We provide a sound and complete algorithm for identification of Dynamic Causal Networks, namely, for computing the effect of an intervention or experiment, based on passive observations only, whenever possible. We note the existence of two types of confounder variables that affect in substantially different ways the identification
procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs. We further propose a procedure
for the transportability of causal effects in Dynamic Causal Network settings, where the result of causal experiments in a source domain may be used for the identification of causal effects in a target domain.Preprin
Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail. Ideally, causal
models of the same system should be consistent with one another in the sense
that they agree in their predictions of the effects of interventions. We
formalise this notion of consistency in the case of Structural Equation Models
(SEMs) by introducing exact transformations between SEMs. This provides a
general language to consider, for instance, the different levels of description
in the following three scenarios: (a) models with large numbers of variables
versus models in which the `irrelevant' or unobservable variables have been
marginalised out; (b) micro-level models versus macro-level models in which the
macro-variables are aggregate features of the micro-variables; (c) dynamical
time series models versus models of their stationary behaviour. Our analysis
stresses the importance of well specified interventions in the causal modelling
process and sheds light on the interpretation of cyclic SEMs.Comment: equal contribution between Rubenstein and Weichwald; accepted
manuscrip
Beyond Structural Causal Models: Causal Constraints Models
Structural Causal Models (SCMs) provide a popular causal modeling framework.
In this work, we show that SCMs are not flexible enough to give a complete
causal representation of dynamical systems at equilibrium. Instead, we propose
a generalization of the notion of an SCM, that we call Causal Constraints Model
(CCM), and prove that CCMs do capture the causal semantics of such systems. We
show how CCMs can be constructed from differential equations and initial
conditions and we illustrate our ideas further on a simple but ubiquitous
(bio)chemical reaction. Our framework also allows to model functional laws,
such as the ideal gas law, in a sensible and intuitive way.Comment: Published in Proceedings of the 35th Annual Conference on Uncertainty
in Artificial Intelligence (UAI-19
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