Temporal inference of probabilistic boolean networks

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

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

This paper presents a new method of fitting probabilistic Boolean networks (PBNs) to time-course state data. The critical issue to be addressed is to identify the contributions of the PBN's constituent Boolean networks in a sequence of temporal data. The sequence must be partitioned into sections, each corresponding to a single model with fixed parameters. We propose an approach to subsequence identification based on 'purity functions' derived from state transition tables, to be used in conjunction with a method for the identification of predictor genes and functions. We also present the estimation of the network switching probability, selection probabilities, perturbation rate, as well as observations on the inference of input genes, predictor functions and their relation with the length of the observed data sequence.
Original languageEnglish
Pages71-72
Number of pages2
DOIs
Publication statusPublished - 2006
Event2006 IEEE International Workshop on Genomic Signal Processing and Statstics - Texas, United States
Duration: 28 May 200630 May 2006

Conference

Conference2006 IEEE International Workshop on Genomic Signal Processing and Statstics
Abbreviated titleGENSIPS 2006
CountryUnited States
CityTexas
Period28/05/0630/05/06

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Keywords

  • probabilistic boolean networks
  • temporal inference
  • signal processing

Cite this

Marshall, S., Yu, L., Xiao, Y., & Dougherty, E. (2006). Temporal inference of probabilistic boolean networks. 71-72. Paper presented at 2006 IEEE International Workshop on Genomic Signal Processing and Statstics, Texas, United States. https://doi.org/10.1109/GENSIPS.2006.353161