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Issue No. 05 - Sept.-Oct. (2012 vol. 9)
ISSN: 1545-5963
pp: 1352-1365
Sucheendra K. Palaniappan , Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore
S. Akshay , IRISA, ENS Cachan Bretagne, Rennes, France
Bing Liu , Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore
Blaise Genest , IRISA, Rennes, France
P. S. Thiagarajan , Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore
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.
probability, belief networks, bioinformatics, bioinformatics, hybrid factored frontier algorithm, dynamic Bayesian networks, biopathway application, probabilistic distribution dynamic models, biochemical networks, Boyen-Koller algorithm, spikes, global states, DBN models, Biological system modeling, Mathematical model, Trajectory, Approximation algorithms, Probability distribution, Approximation methods, Computational modeling, life and medical sciences—biology and genetics., Probability and statistics, symbolic and algebraic manipulation—algorithms

B. Genest, Bing Liu, S. Akshay, S. K. Palaniappan and P. S. Thiagarajan, "A Hybrid Factored Frontier Algorithm for Dynamic Bayesian Networks with a Biopathways Application," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 1352-1365, 2012.
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