Protein
Complex Identification by Supervised Graph Local Clustering
Yanjun Qi, Fernanda Balem, Christos Faloutsos, Judith Klein-Seetharaman, Ziv Bar-Joseph Department of Structural Biology, |
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Motivation: Protein complexes integrate multiple
gene products to coordinate many biological functions. Given a graph
representing pairwise protein interaction data one can search for subgraphs
representing protein complexes. Previous methods for performing such search
relied on the assumption that complexes form a clique in that graph. While
this assumption is true for some complexes, it does not hold for many others.
New algorithms are required in order to recover complexes with other types of
topological structure. Results: We present an algorithm for inferring
protein complexes from weighted interaction graphs. By using graph
topological patterns and biological properties as features, we model each
complex subgraph by a probabilistic Bayesian Network (BN). We use a training
set of known complexes to learn the parameters of this BN model. The
log-likelihood ratio derived from the BN is then used to score subgraphs in
the protein interaction graph and identify new complexes. We applied our
method to protein interaction data in yeast. As we show our algorithm
achieved a considerable improvement over clique based algorithms in terms of
its ability to recover known complexes. We discuss some of the new complexes
predicted by our algorithm and determine that they likely represent true
complexes. l
Due
to the length limitation of the main text, we put some details in this
supporting website. |
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Matlab
Implementation is available @ download
(tar xvf ) ·
Plan
to convert the code into one Cytoscape
plug-in, please check back for updates. |
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Details
about feature sets |
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Some
implementation details l
about
our evaluation experimental setting l
about
our search algorithm l
about
complexity |
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The
MIPS complexes we used as references were extracted from two files: complexCat complexScheme (high-throughput complexes not used) ·
The
TAP complexes we used as references were extracted from the paper’s
supplementary data file; we put one copy here for the
reader’s convenience |