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

• Published in: BMC bioinformatics Vol. 15; no. 1; p. 204
• Main Authors: Hanna, Eileen Marie, Zaki, Nazar
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 19.06.2014; BioMed Central
• Subjects: Algorithms; Bridges; Cluster Analysis; Experimental methods; Humans; Internet; Methods; Protein Interaction Mapping - methods; Protein Interaction Maps; Proteins; Proteins - metabolism; Ratings & rankings; Yeast
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-15-204

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. 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. 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.