By Topic

An improved identification technique of gene regulatory network from gene expression time series data using multi-objective differential evolution

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Datta, D. ; Dept. of Electron. & Telecommun., Jadavpur Univ., Kolkata, India ; Konar, A. ; Nagar, A. ; Bisoyi, A.

Gene regulatory network provides the knowledge of interaction strength among the genes in living organisms. Accurate identification of gene regulatory network is of prime interest to the researchers in recent time. Different researchers applied different optimization techniques to solve this problem. Most of these optimization techniques considers the square error between reference and simulated gene expression as their objective and minimize it to get a solution for the identification problem under consideration. But these techniques do not guarantee a unique set of network parameter, because the squared error is a non-linear multimodal surface of network parameters. Therefore considering only square error as the objective function is not a good choice. An alternative way of formulation of this problem is to validate it from different perspective. In this paper, we propose a technique for identification of gene regulatory network using multiple objectives. The objectives are designed to make the identification technique more robust. Multi-objective differential evolution is used to find a set of pareto-optimal solutions with respect to the objective functions. Among those solutions, one is chosen according to some suitable criterion. Computer simulation has shown that the proposed technique can identify useful interaction information from gene expression time series data.

Published in:

Hybrid Intelligent Systems (HIS), 2010 10th International Conference on

Date of Conference:

23-25 Aug. 2010