18,265 research outputs found
Identifying interactions in the time and frequency domains in local and global networks : a Granger causality approach
Background
Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.
Results
Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered.
Conclusions
The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data
Nonparametric maximum likelihood approach to multiple change-point problems
In multiple change-point problems, different data segments often follow
different distributions, for which the changes may occur in the mean, scale or
the entire distribution from one segment to another. Without the need to know
the number of change-points in advance, we propose a nonparametric maximum
likelihood approach to detecting multiple change-points. Our method does not
impose any parametric assumption on the underlying distributions of the data
sequence, which is thus suitable for detection of any changes in the
distributions. The number of change-points is determined by the Bayesian
information criterion and the locations of the change-points can be estimated
via the dynamic programming algorithm and the use of the intrinsic order
structure of the likelihood function. Under some mild conditions, we show that
the new method provides consistent estimation with an optimal rate. We also
suggest a prescreening procedure to exclude most of the irrelevant points prior
to the implementation of the nonparametric likelihood method. Simulation
studies show that the proposed method has satisfactory performance of
identifying multiple change-points in terms of estimation accuracy and
computation time.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1210 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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