Title : ( Genetic regulatory network inference using Recurrent Neural Networks trained by a multi agent system )
Authors: Adel Ghazikhani , Mohammad Reza Akbarzadeh Totonchi , Reza Monsefi ,
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Abstract
We propose a novel algorithm for gene regulatory network inference. Gene Regulatory Network (GRN) inference is approximating the combined effect of different genes in a specific genome data. GRNs are nonlinear, dynamic and noisy. Time-series data has been frequently used for GRN modeling. Due to the function approximation and feedback nature of GRN, a Recurrent Neural Network (RNN) model is used. RNN training is a complicated task. We propose a multi agent system for RNN training. The agents of the proposed multi agent system trainer are separate swarms of particles building up a multi population Particle Swarm Optimization (PSO) algorithm. We compare the proposed algorithm with a similar algorithm that uses RNN with standard PSO for training. The results show improvements using the E. coli SOS dataset.
Keywords
, Gene Regulatory Network Inference, Particle Swarm Optimization, Multi Population PSO, Recurrent Neural Networks, Multi Agent Systems
@inproceedings{paperid:1042110,
author = {Ghazikhani, Adel and Akbarzadeh Totonchi, Mohammad Reza and Monsefi, Reza},
title = {Genetic regulatory network inference using Recurrent Neural Networks trained by a multi agent system},
booktitle = {Computer and Knowledge Engineering (ICCKE), 2011 1st International eConference on},
year = {2011},
location = {IRAN},
keywords = {Gene Regulatory Network Inference; Particle Swarm Optimization; Multi Population PSO; Recurrent Neural Networks;
Multi Agent Systems},
}
%0 Conference Proceedings
%T Genetic regulatory network inference using Recurrent Neural Networks trained by a multi agent system
%A Ghazikhani, Adel
%A Akbarzadeh Totonchi, Mohammad Reza
%A Monsefi, Reza
%J Computer and Knowledge Engineering (ICCKE), 2011 1st International eConference on
%D 2011
