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)

Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm

2017 ◽  
Vol 15 (04) ◽  
pp. 1750016 ◽  
Author(s):  
Sudip Mandal ◽  
Goutam Saha ◽  
Rajat Kumar Pal
Keyword(s):  
Neural Network ◽  
Gene Regulatory Network ◽  
Recurrent Neural Network ◽  
Regulatory Network ◽  
Search Algorithm ◽  
Biological Database ◽  
Inference Problem ◽  
Fitness Value ◽  
Gene Regulatory ◽  
Time Accuracy

Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.

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