DocumentCode :
2442472
Title :
Temporal inference of probabilistic Boolean networks
Author :
Marshall, S. ; Yu, L. ; Xiao, Y. ; Dougherty, E.R.
Author_Institution :
Dept. of Electron. & Electr. Eng., Univ. of Strathclyde, Glasgow
fYear :
2006
fDate :
28-30 May 2006
Firstpage :
71
Lastpage :
72
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.
Keywords :
Boolean functions; biology computing; genetics; probability; sequences; bioinformatics; gene expression; network switching probability; probabilistic Boolean network; purity function; selection probability; state transition table; subsequence identification; temporal inference; time-course state data; Bioinformatics; Computational biology; Data analysis; Genetics; Genomics; Neural networks; Predictive models; Sequences; Sparse matrices; Switches;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on
Conference_Location :
College Station, TX
Print_ISBN :
1-4244-0384-7
Electronic_ISBN :
1-4244-0385-5
Type :
conf
DOI :
10.1109/GENSIPS.2006.353161
Filename :
4161782
Link To Document :
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