Effect of parameter variations on the inference of context-sensitive probabilisitic boolean networks

Marshall, S. and Yu, L. and Xiao, Y. and Dougherty, E. (2007) Effect of parameter variations on the inference of context-sensitive probabilisitic boolean networks. In: 5th IEEE International Workshop on Genomic Signal Processing and Statistics, 2007-06-10 - 2007-06-12. (https://doi.org/10.1109/GENSIPS.2007.4365839)

Abstract

This paper presents the results of an investigation into the effect of parameter variation on model inference from gene expression data. The models in question are context sensitive Probabilistic Boolean Networks. It is usually necessary to observe a large number of sample points in order to infer the model parameters accurately. This is because the data can become trapped in some fixed point attractor cycles for long time periods. To tackle this problem, a novel sampling strategy for model inference also has been introduced in the paper.

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• Item metadata

  Item type:	Conference or Workshop Item(Paper)
  ID code:	11947
  Dates:	Date Event 2007 Published
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering
  Depositing user:	Strathprints Administrator
  Date deposited:	06 Dec 2011 13:48
  Last modified:	09 Apr 2024 05:10
  URI:	https://strathprints.strath.ac.uk/id/eprint/11947