scholarly journals Gene regulatory network model identification using artificial bee colony and swarm intelligence

2012 ◽  
Author(s):  
Zary Forghany ◽  
Mohsen Davarynejad ◽  
B. Ewa Snaar-Jagalska
Keyword(s):  
Swarm Intelligence ◽  
Gene Regulatory Network ◽  
Network Model ◽  
Regulatory Network ◽  
Artificial Bee Colony ◽  
Model Identification ◽  
Bee Colony ◽  
Gene Regulatory

scholarly journals Inferring Gene Regulatory Network with Recurrent Neural Network and Extended Artificial Bee Colony Algorithm

2018 ◽  
Vol 28 (2) ◽  
pp. 237-250
Author(s):  
Tausif Al Hossain ◽  
Mohammad Shoyaib ◽  
Saifuddin Md Tareeq
Keyword(s):  
Neural Network ◽  
Gene Regulatory Network ◽  
Recurrent Neural Network ◽  
Regulatory Network ◽  
Artificial Bee Colony Algorithm ◽  
Local Minimum ◽  
Artificial Bee Colony ◽  
Computational Techniques ◽  
Bee Colony ◽  
Gene Regulatory

Gene regulatory network is the network of genes interacting with each other performing as functional circuitry inside a cell. Many cellular processes are controlled by this network as they govern the expression levels of genes or gene product. High performance computational techniques are needed to analyze these data as it is heavily affected by noise. There are a number of algorithms available in the literature which use recurrent neural network for model building together with differential evolution, particle swarm optimization or genetic algorithm for searching the regulatory network. The problem with these methods is that they may trap in the local minimum. In this paper, we present an algorithm using recurrent neural network as model and an extended artificial bee colony algorithm for searching regulatory network that can avoid local minimum. A comprehensive analysis on both artificial and real data shows the effectiveness of the proposed approach. Furthermore we have also varied the network dimension and the noise level present in gene expression profiles. The reconstruction method has successfully predicted the underlying network topology while maintaining high accuracy. The proposed approach has also been applied to the real expression data of SOS DNA repair system in Escherichia coli and successfully predicted important regulations. Plant Tissue Cult. & Biotech. 28(2): 237-250, 2018 (December)

Construction of a Molecular Learning Network

2013 ◽  
Vol 17 (6) ◽  
pp. 913-918
Author(s):  
Tomohiro Shirakawa ◽  
◽  
Hiroshi Sato
Keyword(s):  
Associative Learning ◽  
Gene Regulatory Network ◽  
20Th Century ◽  
Network Model ◽  
Regulatory Network ◽  
Learning Ability ◽  
Learning Network ◽  
Unicellular Organisms ◽  
Gene Regulatory

Learning ability in unicellular organisms has been studied since the first half of the 20th century, but there is still no clear evidence of unicellular learning. Based on results from previous associative learning experiments using thePhysarumplasmodium, a gene regulatory network model of unicellular learning was constructed. The model demonstrates that, in principle, unicellular learning can be achieved through the cooperation of several biomolecules.

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