Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probabilistic methods for predicting protein functions in
  protein-protein interaction networks   We discuss probabilistic methods for predicting protein functions from
protein-protein interaction networks. Previous work based on Markov Randon
Fields is extended and compared to a general machine-learning theoretic
approach. Using actual protein interaction networks for yeast from the MIPS
database and GO-SLIM function assignments, we compare the predictions of the
different probabilistic methods and of a standard support vector machine. It
turns out that, with the currently available networks, the simple methods based
on counting frequencies perform as well as the more sophisticated approaches.

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The categories of this paper are: cs
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: physics
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The categories of this paper are: hep-ex
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The categories of this paper are: math
16
The categories of this paper are: stat
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The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org