Document Type
Article
Publication Date
2014
Abstract
Most biological processes are carried out by protein complexes. A substantial number of false positives of the protein-protein interaction (PPI) data can compromise the utility of the datasets for complexes reconstruction. In order to reduce the impact of such discrepancies, a number of data integration and affinity scoring schemes have been devised. The methods encode the reliabilities (confidence) of physical interactions between pairs of proteins. The challenge now is to identify novel and meaningful protein complexes fromthe weighted PPI network. To address this problem, a novel protein complex mining algorithm ClusterBFS (Cluster with Breadth-First Search) is proposed. Based on the weighted density, ClusterBFS detects protein complexes of the weighted network by the breadth first search algorithm, which originates from a given seed protein used as starting-point. The experimental results show that ClusterBFS performs significantly better than the other computational approaches in terms of the identification of protein complexes.
Recommended Citation
Tang, Xiwei; Wang, Jianxin; Li, Min; He, Yiming; and Pan, Yi, "A Novel Algorithm for Detecting Protein Complexes with the Breadth First Search" (2014). Computer Science Faculty Publications. 24.
https://scholarworks.gsu.edu/computer_science_facpub/24
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Comments
Originally Published in:
Biomed Res Int, 2014 354539. DOI: 10.1155/2014/354539