Abstract

There is an urgent need for tools to unravel the complex interactions and functionalities of genes. As such, there has been much interest in reverse-engineering genetic regulatory networks from time series gene expression data. We use an artificial neural network to model the dynamics of complicated gene networks and to learn their parameters. The positive and negative regulations of genes are defined by a weight matrix, and different genes are allowed to have different decaying time constants. We demonstrate the effectiveness of the method by recreating the SOS DNA Repair network of Escherichia coli bacterium, previously discovered through experimental data.

Meeting Name

27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 (2005: Sep. 1-4, Shanghai, China)

Department(s)

Biological Sciences

Second Department

Electrical and Computer Engineering

International Standard Book Number (ISBN)

9780780387409

International Standard Serial Number (ISSN)

0589-1019

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link

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