Gene network inference using a swarm intelligence framework

Authors:
Kyriakos Kentzoglanakis;Matthew Poole
Affiliations:
University of Portsmouth, Portsmouth, United Kingdom;University of Portsmouth, Portsmouth, United Kingdom
Venue:
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Year:
2009

Citing 3
Cited 0

Directed scale-free graphs

SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Discovering Gene Networks with a Neural-Genetic Hybrid

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a framework for inferring gene regulatory networks from gene expression time series. A model-based approach is adopted, according to which the quality of a candidate architecture is evaluated by assessing the ability of the corresponding trained model to reproduce the available dynamics. Candidate architectures are generated in the context of the ant colony optimization (ACO) meta-heuristic and model training is performed using particle swarm optimization (PSO). We propose a novel solution construction heuristic for artificial ants, based on growth and preferential attachment, in order to generate candidate structures that adhere to well-known gene network properties. Preliminary results using an artificial network demonstrate the potential of the framework to infer the underlying network architecture to a promising degree of success.