Title

A Study of Particle Swarm Optimization in Gene Regulatory Networks Inference

Abstract

Gene regulatory inference from time series gene expression data, generated from DNA microarray, has become increasingly important in investigating genes functions and unveiling fundamental cellular processes. Computational methods in machine learning and neural networks play an active role in analyzing the obtained data. Here, we investigate the performance of particle swarm optimization (PSO) on the reconstruction of gene networks, which is modeled with recurrent neural networks (RNN). The experimental results on a synthetic data set are presented to show the parameter effects of PSO on RNN training and the effectiveness of the proposed method in revealing the gene relations.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Gene Regulatory Inference; Genetic Engineering; Optimization; Particle Swarm Optimization (PSO)

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2006 Springer-Verlag, All rights reserved.


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