DocumentCode :
1497762
Title :
A Hybrid Factored Frontier Algorithm for Dynamic Bayesian Networks with a Biopathways Application
Author :
Palaniappan, Sucheendra K. ; Akshay, S. ; Liu, Bing ; Genest, Blaise ; Thiagarajan, P.S.
Author_Institution :
Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore
Volume :
9
Issue :
5
fYear :
2012
Firstpage :
1352
Lastpage :
1365
Abstract :
Dynamic Bayesian Networks (DBNs) can serve as succinct probabilistic dynamic models of biochemical networks [1]. To analyze these models, one must compute the probability distribution over system states at a given time point. Doing this exactly is infeasible for large models; hence one must use approximate algorithms. The Factored Frontier algorithm (FF) is one such algorithm [2]. However FF as well as the earlier Boyen-Koller (BK) algorithm [3] can incur large errors. To address this, we present a new approximate algorithm called the Hybrid Factored Frontier (HFF) algorithm. At each time slice, in addition to maintaining probability distributions over local states-as FF does-HFF explicitly maintains the probabilities of a number of global states called spikes. When the number of spikes is 0, we get FF and with all global states as spikes, we get the exact inference algorithm. We show that by increasing the number of spikes one can reduce errors while the additional computational effort required is only quadratic in the number of spikes. We validated the performance of HFF on large DBN models of biopathways. Each pathway has more than 30 species and the corresponding DBN has more than 3,000 nodes. Comparisons with FF and BK show that HFF is a useful and powerful approximate inferencing algorithm for DBNs.
Keywords :
belief networks; bioinformatics; probability; Boyen-Koller algorithm; DBN models; biochemical networks; bioinformatics; biopathway application; dynamic Bayesian networks; global states; hybrid factored frontier algorithm; probabilistic distribution dynamic models; spikes; Approximation algorithms; Approximation methods; Biological system modeling; Computational modeling; Mathematical model; Probability distribution; Trajectory; Probability and statistics; life and medical sciences—biology and genetics.; symbolic and algebraic manipulation—algorithms; Algorithms; Bayes Theorem; Models, Statistical; Signal Transduction;
fLanguage :
English
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
Publisher :
ieee
ISSN :
1545-5963
Type :
jour
DOI :
10.1109/TCBB.2012.60
Filename :
6185535
Link To Document :
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