A neural network based modeling and validation approach for identifying gene regulatory networks

Knott, S., Mostafavi, S., Mousavi, P. (2010) A neural network based modeling and validation approach for identifying gene regulatory networks. Neurocomputing, 73 (13-15). pp. 2419-2429.

URL: http://www.sciencedirect.com/science/article/pii/S...
DOI: 10.1016/j.neucom.2010.04.018

Abstract

We present a comprehensive neural network based modeling and validation framework for inferring regulatory interactions from temporal gene expression data. We introduce gene set stochastic sampling and sensitivity analysis as two methods for identifying minimal regulatory elements of a target gene expression profile. We test the accuracy of these methods on a simulated dataset, and a biological animal model. A thorough computational approach is also presented to test the validity and robustness of the inferred regulations. We demonstrate that our modeling framework is able to accurately capture the majority of the known interactions in both the simulated and biological data. © 2010 Elsevier B.V.

Item Type: Paper
Uncontrolled Keywords: Gene regulatory networks Neural networks Reverse engineering
Subjects: bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene regulation
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene regulation

organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks
CSHL Authors:
Communities: CSHL Post Doctoral Fellows
CSHL labs > Hannon lab
Depositing User: Matt Covey
Date Deposited: 18 Mar 2015 15:05
Last Modified: 18 Mar 2015 15:05
URI: http://repository.cshl.edu/id/eprint/31278

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