Inferring the perturbation time from biological time course data

MOTIVATION Time course data are often used to study the changes to a biological process after perturbation. Statistical methods have been developed to determine whether such a perturbation induces changes over time, e.g. comparing a perturbed and unperturbed time course dataset to uncover differences. However, existing methods do not provide a principled statistical approach to identify the spe...

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To “ Inferring Network Structure from Interventional Time - Course Experiments

Furthermore this orthogonalisation can be used to improve computational effeciency when comparing a large number of models with the same X0. Define x ′ = (In − P 0)x (which can be precomputed) and then assuming that X0Xγ = 0a×b, we have x T (In − P 0 − P γ)x = x′T (In − P γ)x. This reduces the number of computations that have to be performed for each model matrix Xγ . When the only parameter co...

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Supplement to “ Inferring Network Structure from Interventional Time - Course Experiments ”

Furthermore this orthogonalisation can be used to improve computational effeciency when comparing a large number of models with the same X0. Define x ′ = (In − P 0)x (which can be precomputed) and then assuming that X0Xγ = 0a×b, we have x T (In − P 0 − P γ)x = x′T (In − P γ)x. This reduces the number of computations that have to be performed for each model matrix Xγ . When the only parameter co...

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Inferring Network Structure from Interventional Time-course Experiments by Simon

Copyright and reuse: The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP ...

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Inferring Functional Pathways from Multi-Perturbation Data

BACKGROUND Recently, a conceptually new approach for analyzing gene networks, the Functional Influence Network (FIN) was presented. The FIN approach uses the measured performance of a given cellular function under different multi-perturbations, to identify the main functional pathways and interactions underlying its processing. Here we present and study an iterative, extended version of FIN, th...

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Inferring the perturbation time from biological time course data

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