Please use this identifier to cite or link to this item: https://doi.org/10.1109/TCBB.2012.60
Title: A hybrid factored frontier algorithm for dynamic bayesian networks with a biopathways application
Authors: Palaniappan, S.K.
Akshay, S.
Liu, B. 
Genest, B.
Thiagarajan, P.S. 
Keywords: life and medical sciences-biology and genetics
Probability and statistics
symbolic and algebraic manipulation-algorithms
Issue Date: 2012
Citation: Palaniappan, S.K., Akshay, S., Liu, B., Genest, B., Thiagarajan, P.S. (2012). A hybrid factored frontier algorithm for dynamic bayesian networks with a biopathways application. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9 (5) : 1352-1365. ScholarBank@NUS Repository. https://doi.org/10.1109/TCBB.2012.60
Abstract: Dynamic Bayesian Networks (DBNs) can serve as succinct probabilistic dynamic models of biochemical networks [CHECK END OF SENTENCE]. 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 [CHECK END OF SENTENCE]. However FF as well as the earlier Boyen-Koller (BK) algorithm [CHECK END OF SENTENCE] 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. © 2004-2012 IEEE.
Source Title: IEEE/ACM Transactions on Computational Biology and Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/39314
ISSN: 15455963
DOI: 10.1109/TCBB.2012.60
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