Here, we have proposed a statistical framework based on a novel bat algorithm inspired particle swarm optimisation algorithm for the reconstruction of gene regulatory networks from temporal gene expression data. The recurrent neural network formalism has been implemented to extract the underlying dynamics from time series microarray datasets accurately. The proposed swarm intelligence framework has been used for optimising the parameters of the recurrent neural network model. Preliminary research with the proposed methodology has been done on a small, artificial network and the experimental (in vivo) microarray data of the SOS DNA repair network of Escherichia coli. Results obtained suggest that the proposed methodology can infer the underlying network structures with a better degree of success. © 2015 IEEE.

RAJAT PAL

Computer Science and Engineering

A KHAN

P DATTA

G SAHA

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Gene regulatory networks using bat algorithm inspired particle swarm optimization

2015 IEEE International WIE Conference on Electrical and Computer Engineering, WIECON-ECE 2015

The accurate reconstruction of gene regulatory networks from temporal gene expression data is crucial for the identification of genetic inter-regulations at the cellular level. This will help us to comprehend the working of living entities properly. Here, we have proposed a novel quantum computing based technique for the reverse engineering of gene regulatory networks from time-series genetic expression datasets. The dynamics of the temporal expression profiles have been modelled using the recurrent neural network formalism. The corresponding training of model parameters has been realised with the help of the proposed quantum computing methodology based concepts. This is based on entanglement and decoherence concepts. The application of quantum computing technique in this domain of research is comparatively new. The results obtained using this technique is highly satisfactory. We have applied it to a 4-gene artificial genetic network model, which was previously studied by other researchers. Also, a 10-gene and a 20-gene genetic network have been studied using the proposed technique. The obtained results suggest that quantum computing technique significantly reduces the computational time, retaining the accuracy of the inferred gene regulatory networks to a comparatively satisfactory level. © Springer International Publishing AG 2017.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Quantum computing based inference of GRNs

IEEE/ACM Transactions on Computational Biology and Bioinformatics

A Swarm Intelligence Framework for Reconstructing Gene Networks: Searching for Biologically Plausible Architectures

Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) Studies in Computational Intelligence

A New Metaheuristic Bat-Inspired Algorithm

Neural Networks

Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization

Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization

Wiley Series in Probability and Statistics

Analyzing Microarray Gene Expression Data

Journal	Data powered by Typeset2015 IEEE International WIE Conference on Electrical and Computer Engineering, WIECON-ECE 2015
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No