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Detecting protein complexes in protein interaction networks using a ranking algorithm with a refined merging procedure.

Hanna EM, Zaki N - BMC Bioinformatics (2014)

Bottom Line: It is important since it allows better understanding of cellular functions as well as malfunctions and it consequently leads to producing more effective cures for diseases.ProRank + was compared to several state-of-the-art approaches in order to show its effectiveness.The experimental results achieved by ProRank + show its ability to detect protein complexes in protein interaction networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Information Technology, United Arab Emirates University (UAEU, Al Ain 17551, United Arab Emirates. eileen.hanna@uaeu.ac.ae.

ABSTRACT

Background: Developing suitable methods for the identification of protein complexes remains an active research area. It is important since it allows better understanding of cellular functions as well as malfunctions and it consequently leads to producing more effective cures for diseases. In this context, various computational approaches were introduced to complement high-throughput experimental methods which typically involve large datasets, are expensive in terms of time and cost, and are usually subject to spurious interactions.

Results: In this paper, we propose ProRank+, a method which detects protein complexes in protein interaction networks. The presented approach is mainly based on a ranking algorithm which sorts proteins according to their importance in the interaction network, and a merging procedure which refines the detected complexes in terms of their protein members. ProRank + was compared to several state-of-the-art approaches in order to show its effectiveness. It was able to detect more protein complexes with higher quality scores.

Conclusions: The experimental results achieved by ProRank + show its ability to detect protein complexes in protein interaction networks. Eventually, the method could potentially identify previously-undiscovered protein complexes.The datasets and source codes are freely available for academic purposes at http://faculty.uaeu.ac.ae/nzaki/Research.htm.

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A yeast protein-protein interaction sub-network. The nodes coloured in yellow correspond to essential proteins identified by the ProRank algorithm.
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Figure 2: A yeast protein-protein interaction sub-network. The nodes coloured in yellow correspond to essential proteins identified by the ProRank algorithm.

Mentions: In addition to the steps mentioned above, the ProRank algorithm discards formed protein complexes of less than three members. Also, it merges two complexes if more than 50% of the neighbors of each protein belonging to the first complex are in the second complex. To show the potential of the approach, we consider the network presented in Figure 2. It is a sub-network generated from the yeast protein-protein interaction dataset at the Mentha interactome browser [21], version date 05/01/2014. The sub-network includes of 235 interactions. It corresponds to the largest connected portion of the network consisting of proteins which participate in the interactions of scores greater than or equal to 0.99, and their inter-connections of scores greater than or equal to 0.8. The nodes colored in yellow highlight the essential proteins identified by ProRank and the resulting protein complexes are presented in the first row of Figure 3.


Detecting protein complexes in protein interaction networks using a ranking algorithm with a refined merging procedure.

Hanna EM, Zaki N - BMC Bioinformatics (2014)

A yeast protein-protein interaction sub-network. The nodes coloured in yellow correspond to essential proteins identified by the ProRank algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4230023&req=5

Figure 2: A yeast protein-protein interaction sub-network. The nodes coloured in yellow correspond to essential proteins identified by the ProRank algorithm.
Mentions: In addition to the steps mentioned above, the ProRank algorithm discards formed protein complexes of less than three members. Also, it merges two complexes if more than 50% of the neighbors of each protein belonging to the first complex are in the second complex. To show the potential of the approach, we consider the network presented in Figure 2. It is a sub-network generated from the yeast protein-protein interaction dataset at the Mentha interactome browser [21], version date 05/01/2014. The sub-network includes of 235 interactions. It corresponds to the largest connected portion of the network consisting of proteins which participate in the interactions of scores greater than or equal to 0.99, and their inter-connections of scores greater than or equal to 0.8. The nodes colored in yellow highlight the essential proteins identified by ProRank and the resulting protein complexes are presented in the first row of Figure 3.

Bottom Line: It is important since it allows better understanding of cellular functions as well as malfunctions and it consequently leads to producing more effective cures for diseases.ProRank + was compared to several state-of-the-art approaches in order to show its effectiveness.The experimental results achieved by ProRank + show its ability to detect protein complexes in protein interaction networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Information Technology, United Arab Emirates University (UAEU, Al Ain 17551, United Arab Emirates. eileen.hanna@uaeu.ac.ae.

ABSTRACT

Background: Developing suitable methods for the identification of protein complexes remains an active research area. It is important since it allows better understanding of cellular functions as well as malfunctions and it consequently leads to producing more effective cures for diseases. In this context, various computational approaches were introduced to complement high-throughput experimental methods which typically involve large datasets, are expensive in terms of time and cost, and are usually subject to spurious interactions.

Results: In this paper, we propose ProRank+, a method which detects protein complexes in protein interaction networks. The presented approach is mainly based on a ranking algorithm which sorts proteins according to their importance in the interaction network, and a merging procedure which refines the detected complexes in terms of their protein members. ProRank + was compared to several state-of-the-art approaches in order to show its effectiveness. It was able to detect more protein complexes with higher quality scores.

Conclusions: The experimental results achieved by ProRank + show its ability to detect protein complexes in protein interaction networks. Eventually, the method could potentially identify previously-undiscovered protein complexes.The datasets and source codes are freely available for academic purposes at http://faculty.uaeu.ac.ae/nzaki/Research.htm.

Show MeSH