Neural networks and temporal gene expression data

Authors:
A. Krishna;A. Narayanan;E. C. Keedwell
Affiliations:
Bioinformatics Laboratory, Washington Singer Laboratories, School of Biological and Chemical Sciences;School of Engineering, Computer Science and Mathematics, University of Exeter, UK;School of Engineering, Computer Science and Mathematics, University of Exeter, UK
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

Citing 2
Cited 0

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Abstract

Temporal gene expression data is of particular interest to systems biology researchers. Such data can be used to create gene networks, where such networks represent the regulatory interactions between genes over time. Reverse engineering gene networks from temporal gene expression data is one of the most important steps in the study of complex biological systems. This paper introduces sensitivity analysis of systematically-perturbed trained neural networks to both select a smaller and more influential subset of genes from a temporal gene expression dataset as well as reverse engineer a gene network from the reduced temporal gene expression data. The methodology was applied to the rat cervical spinal cord development time-course data, and it is demonstrated that the method not only identifies important genes involved in regulatory relationships but also generates candidate gene networks for further experimental study.