Network Inference and Biological Dynamics.

@article{Oates2012NetworkIA,
  title={Network Inference and Biological Dynamics.},
  author={Chris J. Oates and Sach Mukherjee},
  journal={The annals of applied statistics},
  year={2012},
  volume={6 3},
  pages={
          1209-1235
        }
}
Network inference approaches are now widely used in biological applications to probe regulatory relationships between molecular components such as genes or proteins. Many methods have been proposed for this setting, but the connections and differences between their statistical formulations have received less attention. In this paper, we show how a broad class of statistical network inference methods, including a number of existing approaches, can be described in terms of variable selection for… 

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References

SHOWING 1-10 OF 73 REFERENCES
Revealing differences in gene network inference algorithms on the network level by ensemble methods
TLDR
A statistical analysis investigating differences and similarities of four network inference algorithms, ARACNE, CLR, MRNET and RN, with respect to local network-based measures reveals the bias and provides guidance in the interpretation of inferred regulatory networks from expression data.
Statistical inference of the time-varying structure of gene-regulation networks
TLDR
ARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.
Network inference using informative priors
TLDR
This article addresses the question of incorporating prior information into network inference with focus on directed models called Bayesian networks, and introduces prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity.
Using Bayesian Networks to Analyze Expression Data
TLDR
This paper proposes a new framework for discovering interactions between genes based on multiple expression measurements, and presents an efficient algorithm capable of learning such networks and statistical method to assess confidence in their features.
Evaluating functional network inference using simulations of complex biological systems
TLDR
The first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data is proposed and found that the algorithm is highly effective at recovering the functional network structure of the simulated system-including the irrelevance of unregulated variables-from sampled data alone.
Granger causality vs. dynamic Bayesian network inference: a comparative study
TLDR
A systematic and computationally intensive comparison between the two approaches to the causal relationship among different elements based upon multi-dimensional temporal data shows that the dynamic Bayesian network inference performs better than the Granger causality approach when the data length is short.
A Bayesian approach to reconstructing genetic regulatory networks with hidden factors
TLDR
Variational approximations are used to perform the analogous model selection task in the Bayesian context and place JunB and JunD at the centre of the mechanisms that control apoptosis and proliferation.
Learning biological networks: from modules to dynamics.
TLDR
An overall strategy for the reconstruction of global networks based on the ability to reconstruct large fractions of prokaryotic regulatory networks from compendiums of genomics data is reviewed, based on results in microbial systems.
Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes
TLDR
The combination of both: dynamic programming and regularization yields an inference procedure that outperforms two alternative established network reconstruction methods from the biology literature.
...
...