Simplified Swarm Optimization-Based Function Module Detection in Protein–Protein Interaction Networks

Xianghan Zheng,Riqing Chen,Lingting Wu,Shaozhen Ye

doi:10.3390/app7040412

Abstract

Proteomics research has become one of the most important topics in the field of life science and natural science. At present, research on protein–protein interaction networks (PPIN) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate detection mechanisms of functional modules in PPIN, including open database, existing detection algorithms, and recent solutions. After that, we describe the proposed approach based on the simplified swarm optimization (SSO) algorithm and the knowledge of Gene Ontology (GO). The proposed solution implements the SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species datasets for experiment: fruitfly, mouse, scere, and human. The testing and analysis result show that the proposed solution is feasible, efficient, and could achieve a higher accuracy of prediction than existing approaches.

Highlights

Proteomics is one of the most important topics in the fields of life science and natural science [1,2,3,4,5]
Since the Particle Swarm Optimization (PSO)-related solution is efficient and has been implemented in different kinds of NP hard problems, we propose the enhancement of PSO, Simplified algorithms, for implementation in enhancement of PSO, Simplified algorithms, for implementation in the the detection of protein function modules
Results show that when the threshold value increases to a certain extent, the unnecessary protein filtration is greatly reduced, and the size of the protein function module (PFM)

Summary

Introduction

Proteomics is one of the most important topics in the fields of life science and natural science [1,2,3,4,5]. Considering that proteins alone rarely exhibit their biological functions in individuals, the understanding of protein–protein interactions (PPI) [6] is the basis of revealing the activity of protein and promotes the study of various diseases and development of new drugs. In the past 10 years, substantial work was conducted to promote the research in the field of PPI, such as publications in Nature [2] and Science [3], proceedings of the National Academy of Sciences [3], and nucleic acid research [7]. Available data on PPI are greatly enriched because of the fast development of high-throughput screening [7] and data mining technologies [8]. High-throughput screening technology generates a huge amount of noisy data and higher false positive rates, while the experimental method loses lots of real interactions

Results

Discussion

Conclusion