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

S. Marshall, L. Yu, Y. Xiao, E. Dougherty

Research output: Contribution to conferencePaper

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.
Original languageEnglish
Pages1-4
Number of pages5
DOIs
Publication statusPublished - 2007
Event5th IEEE International Workshop on Genomic Signal Processing and Statistics - Tuusula, Finland
Duration: 10 Jun 200712 Jun 2007

Conference

Conference5th IEEE International Workshop on Genomic Signal Processing and Statistics
Abbreviated titleGENSIPS 2007
CountryFinland
CityTuusula
Period10/06/0712/06/07

Keywords

  • boolean networks
  • parameter variations
  • genomic signal processing
  • inference
  • inference mechanisms

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    Marshall, S., Yu, L., Xiao, Y., & Dougherty, E. (2007). Effect of parameter variations on the inference of context-sensitive probabilisitic boolean networks. 1-4. Paper presented at 5th IEEE International Workshop on Genomic Signal Processing and Statistics , Tuusula, Finland. https://doi.org/10.1109/GENSIPS.2007.4365839