Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal Queries from Observational Data in Biological Systems via
  Bayesian Networks: An Empirical Study in Small Networks   Biological networks are a very convenient modelling and visualisation tool to
discover knowledge from modern high-throughput genomics and postgenomics data
sets. Indeed, biological entities are not isolated, but are components of
complex multi-level systems. We go one step further and advocate for the
consideration of causal representations of the interactions in living
systems.We present the causal formalism and bring it out in the context of
biological networks, when the data is observational. We also discuss its
ability to decipher the causal information flow as observed in gene expression.
We also illustrate our exploration by experiments on small simulated networks
as well as on a real biological data set.

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The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
1
The categories of this paper are: cs
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci