Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination AlgorithmReport as inadecuate

Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm - Download this document for free, or read online. Document in PDF available to download.

Advances in Bioinformatics - Volume 2016 2016, Article ID 5283937, 9 pages -

Research Article

Department of Electronics and Communication Engineering, Global Institute of Management and Technology, Krishna Nagar, West Bengal 741 102, India

Department of Computer Science and Engineering, University of Calcutta, Kolkata 700 098, India

Department of Information Technology, North-Eastern Hill University, Shillong 793 022, India

Received 2 October 2015; Revised 7 January 2016; Accepted 10 January 2016

Academic Editor: Huixiao Hong

Copyright © 2016 Sudip Mandal 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.


The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

Author: Sudip Mandal, Abhinandan Khan, Goutam Saha, and Rajat K. Pal



Related documents