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.
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: | 2010 |
| Date Deposited: | 18 Mar 2015 15:05 |
| Last Modified: | 18 Mar 2015 15:05 |
| URI: | https://repository.cshl.edu/id/eprint/31278 |
Actions (login required)
 |
Administrator's edit/view item |
