ICCKE2011, International Conference on Computer and Knowledge Engineering , 2011-10-13

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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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. Timeseries 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
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@inproceedings{paperid:1026772,
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 = {ICCKE2011, International Conference on Computer and Knowledge Engineering},
year = {2011},
location = {مشهد, IRAN},
keywords = {Gene Regulatory Network Inference; Particle Swarm Optimization; Multi Population PSO; Recurrent Neural Networks; Multi Agent Systems},
}

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%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 ICCKE2011, International Conference on Computer and Knowledge Engineering
%D 2011

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