By Topic

Genetic network inference via gene set stochastic sampling and sensitivity analysis

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)
Knott, S. ; Sch. of Comput., Queen''s Univ., Kingston, Ont. ; Mostafavi, S. ; Mousavi, P. ; Glasgow, J.

In this paper, two approaches utilizing neural networks, intended to infer genetic regulatory networks from temporal gene expression measurements, are examined. These approaches aimed to find a minimal set of genes that were able to accurately predict the expression levels of a given gene, thus modeling the interactions in the underlying genetic regulatory networks. Two neural network architectures were employed in each approach to determine the robustness of the modeling procedure with respect to the network architecture. Two testing procedures were also devised to evaluate the trained neural networks' performance and generalizability. The resulting neural networks predicted, with high accuracy, the target gene expression level at future times given the predicted minimal gene-set expression levels at previous time points

Published in:

Control Applications, 2005. CCA 2005. Proceedings of 2005 IEEE Conference on

Date of Conference:

28-31 Aug. 2005