MAE-FMD: Multi-agent evolutionary method for functional module detection in protein-protein interaction networks
| Autor: | Jia Wei Lv, Lang Jiao, Jun Zhong Ji, Cui Cui Yang, Aidong Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Functional module detection
Theoretical computer science Population Crossover Computational biology Biology Biochemistry Protein protein interaction network Evolution Molecular Fungal Proteins Structural Biology Yeasts Protein Interaction Mapping Humans education Molecular Biology Evolutionary operators Information exchange education.field_of_study Fungal protein Protein-protein interaction network Applied Mathematics Methodology Article Computer Science Applications Multi-agent evolution Ppi network Functional module Algorithms |
| Zdroj: | BMC Bioinformatics |
| ISSN: | 1471-2105 |
| Popis: | Background Studies of functional modules in a Protein-Protein Interaction (PPI) network contribute greatly to the understanding of biological mechanisms. With the development of computing science, computational approaches have played an important role in detecting functional modules. Results We present a new approach using multi-agent evolution for detection of functional modules in PPI networks. The proposed approach consists of two stages: the solution construction for agents in a population and the evolutionary process of computational agents in a lattice environment, where each agent corresponds to a candidate solution to the detection problem of functional modules in a PPI network. First, the approach utilizes a connection-based encoding scheme to model an agent, and employs a random-walk behavior merged topological characteristics with functional information to construct a solution. Next, it applies several evolutionary operators, i.e., competition, crossover, and mutation, to realize information exchange among agents as well as solution evolution. Systematic experiments have been conducted on three benchmark testing sets of yeast networks. Experimental results show that the approach is more effective compared to several other existing algorithms. Conclusions The algorithm has the characteristics of outstanding recall, F-measure, sensitivity and accuracy while keeping other competitive performances, so it can be applied to the biological study which requires high accuracy. |
| Databáze: | OpenAIRE |
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