College of Information Technology, United Arab Emirates University (UAEU, Al Ain 17551, United Arab Emirates.
BMC Bioinformatics. 2014 Jun 19;15:204. 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.
开发合适的蛋白质复合物鉴定方法仍然是一个活跃的研究领域。这很重要，因为它可以帮助我们更好地理解细胞功能以及功能障碍，从而为疾病治疗提供更有效的方法。在这种情况下，引入了各种计算方法来补充通常涉及大数据集的高通量实验方法，这些方法在时间和成本方面都很昂贵，并且通常容易受到虚假相互作用的影响。
在本文中，我们提出了 ProRank+，这是一种用于在蛋白质相互作用网络中检测蛋白质复合物的方法。该方法主要基于一种排序算法，该算法根据蛋白质在相互作用网络中的重要性对蛋白质进行排序，以及一种合并过程，该过程根据蛋白质成员对检测到的复合物进行细化。ProRank+与几种最先进的方法进行了比较，以展示其有效性。它能够检测到更多具有更高质量分数的蛋白质复合物。
ProRank+的实验结果表明它能够在蛋白质相互作用网络中检测蛋白质复合物。最终，该方法可能能够识别以前未发现的蛋白质复合物。数据集和源代码可在 http://faculty.uaeu.ac.ae/nzaki/Research.htm 上免费供学术使用。
