Protein complex detection based on flower pollination mechanism in multi-relation reconstructed dynamic protein networks

12Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Background: Detecting protein complex in protein-protein interaction (PPI) networks plays a significant part in bioinformatics field. It enables us to obtain the better understanding for the structures and characteristics of biological systems. Methods: In this study, we present a novel algorithm, named Improved Flower Pollination Algorithm (IFPA), to identify protein complexes in multi-relation reconstructed dynamic PPI networks. Specifically, we first introduce a concept called co-essentiality, which considers the protein essentiality to search essential interactions, Then, we devise the multi-relation reconstructed dynamic PPI networks (MRDPNs) and discover the potential cores of protein complexes in MRDPNs. Finally, an IFPA algorithm is put forward based on the flower pollination mechanism to generate protein complexes by simulating the process of pollen find the optimal pollination plants, namely, attach the peripheries to the corresponding cores. Results: The experimental results on three different datasets (DIP, MIPS and Krogan) show that our IFPA algorithm is more superior to some representative methods in the prediction of protein complexes. Conclusions: Our proposed IFPA algorithm is powerful in protein complex detection by building multi-relation reconstructed dynamic protein networks and using improved flower pollination algorithm. The experimental results indicate that our IFPA algorithm can obtain better performance than other methods.

Cite

CITATION STYLE

APA

Lei, X., Fang, M., Guo, L., & Wu, F. X. (2019). Protein complex detection based on flower pollination mechanism in multi-relation reconstructed dynamic protein networks. BMC Bioinformatics, 20. https://doi.org/10.1186/s12859-019-2649-0

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free