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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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ProRank + compared to ProRank, MCL, MCODE, CMC, AP, ClusterONE, RNSC, RRW, and CFinder. Here, the four weighted yeast datasets are used: Collins, Krogan core, Krogan extended and Gavin. The comparisons are in terms of (a) the number of clusters that match the reference complexes, (b) the geometric accuracy (Acc) which reflects the clustering-wise sensitivity (Sn) and the clustering-wise positive predictive value (PPV), and (c) the maximum matching ratio (MMR).
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Figure 6: ProRank + compared to ProRank, MCL, MCODE, CMC, AP, ClusterONE, RNSC, RRW, and CFinder. Here, the four weighted yeast datasets are used: Collins, Krogan core, Krogan extended and Gavin. The comparisons are in terms of (a) the number of clusters that match the reference complexes, (b) the geometric accuracy (Acc) which reflects the clustering-wise sensitivity (Sn) and the clustering-wise positive predictive value (PPV), and (c) the maximum matching ratio (MMR).

Mentions: ProRank + was compared to other state-of-the-art methods, applied on the same datasets and evaluated based on the same quality scores. These methods include ProRank [12] to highlight the attained improvement, Markov Clustering (MCL) [2], the molecular complex detection (MCODE) algorithm [3], the clustering based on maximal cliques (CMC) method [4], the Affinity Propagation (AP) algorithm [5], ClusterONE [6], the restricted neighborhood search (RNSC) algorithm [7], the RRW algorithm [9], and CFinder [10]. The comparisons among the results scored by these approaches [6] and those scored by ProRank + are displayed in Figures 6 and 7. Since not all the algorithms can be applied to unweighted datasets, fewer methods for instance were applied on the BioGRID dataset.


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

Hanna EM, Zaki N - BMC Bioinformatics (2014)

ProRank + compared to ProRank, MCL, MCODE, CMC, AP, ClusterONE, RNSC, RRW, and CFinder. Here, the four weighted yeast datasets are used: Collins, Krogan core, Krogan extended and Gavin. The comparisons are in terms of (a) the number of clusters that match the reference complexes, (b) the geometric accuracy (Acc) which reflects the clustering-wise sensitivity (Sn) and the clustering-wise positive predictive value (PPV), and (c) the maximum matching ratio (MMR).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: ProRank + compared to ProRank, MCL, MCODE, CMC, AP, ClusterONE, RNSC, RRW, and CFinder. Here, the four weighted yeast datasets are used: Collins, Krogan core, Krogan extended and Gavin. The comparisons are in terms of (a) the number of clusters that match the reference complexes, (b) the geometric accuracy (Acc) which reflects the clustering-wise sensitivity (Sn) and the clustering-wise positive predictive value (PPV), and (c) the maximum matching ratio (MMR).
Mentions: ProRank + was compared to other state-of-the-art methods, applied on the same datasets and evaluated based on the same quality scores. These methods include ProRank [12] to highlight the attained improvement, Markov Clustering (MCL) [2], the molecular complex detection (MCODE) algorithm [3], the clustering based on maximal cliques (CMC) method [4], the Affinity Propagation (AP) algorithm [5], ClusterONE [6], the restricted neighborhood search (RNSC) algorithm [7], the RRW algorithm [9], and CFinder [10]. The comparisons among the results scored by these approaches [6] and those scored by ProRank + are displayed in Figures 6 and 7. Since not all the algorithms can be applied to unweighted datasets, fewer methods for instance were applied on the BioGRID dataset.

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