Biomedical Engineering Reference
In-Depth Information
intersection and overlap between these source PPI databases are relatively small.
Recently, the integration has been done and can be explored in the web server
called Agile Protein Interaction Data Analyzer (APID) which is an interactive
bioinformatics
web tool developed to allow exploration and analysis of currently
known information about PPIs integrated and uni
'
ed in a common and comparative
platform [
34
].
The Protein Interaction Network Analysis (PINA2.0) platform is a comprehen-
sive web resource, which includes a database of uni
ed PPI data integrated from six
manually curated public databases and a set of built-in tools for network con-
struction,
ltering, analysis, and visualization [
35
,
44
]. The databases and number
of interactions were tabled in Table
1
.
5 Conclusion
In this paper, we have discussed different algorithms to detect protein complexes in
large interaction networks. Protein complexes are signi
cant for recognizing the
principles of cellular organization and function. The motivation behind these
algorithms is to benchmark the clustering techniques and measure their prediction
accuracy to identify the protein complexes. The clustering result of each compu-
tational method can be regarded as a feature that describes the PPI network.
However, most of the approaches rely on the hypothesis that proteins within the
same complex would have relatively more interactions. Protein complexes com-
putation can be measured ef
ciently with the help of precision, recall, F-measure,
co-localization and co-annotation, sensitivity, positive predictive value, and sepa-
ration values, etc. With the help of this protein complex identi
cation, one can
detect
the behavior and closeness of proteins and which will helpful
in drug
detection. The authors declare that there is no con
fl
ict of interests regarding the
publication of this paper.
Acknowledgement The authors published this paper under their Ph.D. work. The authors wish to
thank the University Grants Commission (UGC) for extending nancial support for this study,
under the project
Development of a Software Tool to Identify Lung-Cancer Related Genes using
Protein-Protein Interaction Network
“
”
with sanction F.NO:4-4/2014-15[MRP-SEM/UGC-SERO].
References
1. Tu S, Xu L (2010) A binary matrix factorization algorithm for protein complex prediction. In:
IEEE international conference on bioinformatics and biomedicine workshops (BIBMW),
ISBN: 978-1-4244-8304-4
2. Ou-Yang L, Dai DQ, Zhang XF (2013) Protein complex detection via weighted ensemble
clustering based on Bayesian nonnegative matrix factorization. PLoS One. doi:
10.1371/
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