205 research outputs found
Causality and independence in perfectly adapted dynamical systems
Perfect adaptation in a dynamical system is the phenomenon that one or more
variables have an initial transient response to a persistent change in an
external stimulus but revert to their original value as the system converges to
equilibrium. The causal ordering algorithm can be used to construct an
equilibrium causal ordering graph that represents causal relations and a Markov
ordering graph that implies conditional independences from a set of equilibrium
equations. Based on this, we formulate sufficient graphical conditions to
identify perfect adaptation from a set of first-order differential equations.
Furthermore, we give sufficient conditions to test for the presence of perfect
adaptation in experimental equilibrium data. We apply our ideas to a simple
model for a protein signalling pathway and test its predictions both in
simulations and on real-world protein expression data. We demonstrate that
perfect adaptation in this model can explain why the presence and orientation
of edges in the output of causal discovery algorithms does not always appear to
agree with the direction of edges in biological consensus networks.Comment: 32 page
Bioinformatics tools in predictive ecology: Applications to fisheries
This article is made available throught the Brunel Open Access Publishing Fund - Copygith @ 2012 Tucker et al.There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
While the success of semi-supervised learning (SSL) is still not fully
understood, Sch\"olkopf et al. (2012) have established a link to the principle
of independent causal mechanisms. They conclude that SSL should be impossible
when predicting a target variable from its causes, but possible when predicting
it from its effects. Since both these cases are somewhat restrictive, we extend
their work by considering classification using cause and effect features at the
same time, such as predicting disease from both risk factors and symptoms.
While standard SSL exploits information contained in the marginal distribution
of all inputs (to improve the estimate of the conditional distribution of the
target given inputs), we argue that in our more general setting we should use
information in the conditional distribution of effect features given causal
features. We explore how this insight generalises the previous understanding,
and how it relates to and can be exploited algorithmically for SSL.Comment: 36th Conference on Uncertainty in Artificial Intelligence (2020)
(Previously presented at the NeurIPS 2019 workshop "Do the right thing":
machine learning and causal inference for improved decision making,
Vancouver, Canada.
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