Table of Contents Author Guidelines Submit a Manuscript
Scientifica
Volume 2016 (2016), Article ID 1060843, 14 pages
http://dx.doi.org/10.1155/2016/1060843
Research Article

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence

1Department of Computer Science and Engineering, University of Calcutta, Acharya Prafulla Chandra Roy Siksha Prangan, JD-2, Sector III, Salt Lake City, Kolkata, West Bengal 700 098, India
2Department of Information Technology, North Eastern Hill University, Umshing-Mawkynroh, Shillong, Meghalaya 793 022, India

Received 28 December 2015; Revised 19 April 2016; Accepted 24 April 2016

Academic Editor: Matthias Futschik

Copyright © 2016 Abhinandan Khan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.