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

Authors:
S. Knott;S. Mostafavi;P. Mousavi
Affiliations:
University of Southern California, USA;University of Toronto, Canada;School of Computing, Queen's University, Kingston, ON, Canada
Venue:
Neurocomputing
Year:
2010

Citing 7
Cited 3

Bayesian interpolation

Neural Computation
Molecular biology for computer scientists

Artificial intelligence and molecular biology
A Computational Approach to Reconstructing Gene Regulatory Networks

CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Reconstruction of the genetic regulatory dynamics of the rat spinal cord development: Local Invariants approach

Journal of Biomedical Informatics
Inferring transcriptional regulatory networks from high-throughput data

Bioinformatics
Genetic algorithms for gene expression analysis

EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging

IEEE Transactions on Neural Networks

Questionnaires-based skin attribute prediction using Elman neural network

Neurocomputing
Reverse engineering of gene regulatory networks using flexible neural tree models

Neurocomputing
Robust stability analysis of Markov jump standard genetic regulatory networks with mixed time delays and uncertainties

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

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.