Abstract
Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.
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Notes
- 1.
In more exact terms, given causal interpretation, there is a concordance between independence equivalence (given by the v-structure method) and likelihood equivalence (5).
- 2.
Sachs dataset: consists of flow cytometry measurements from a signaling network with 11 nodes, of which five have been perturbed in some measurements (21). These interventions contain both inhibitions and activations of the nodes. The data was discretized into ternary values, and uniform Dirichlet priors for parameters and uniform structural priors were used.
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Acknowledgements
This work was supported by the Academy of Finland (application numbers 135320 and 213462, Finnish Programme for Centres of Excellence in Research 2006–2011), and FP7 EU project SYBILLA.
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Larjo, A., Shmulevich, I., Lähdesmäki, H. (2013). Structure Learning for Bayesian Networks as Models of Biological Networks. In: Mamitsuka, H., DeLisi, C., Kanehisa, M. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 939. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-107-3_4
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DOI: https://doi.org/10.1007/978-1-62703-107-3_4
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