NDLI: A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways

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A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways

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A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways

Content Provider	ACM Digital Library
Author	Genest, Blaise Palaniappan, Sucheendra K. Thiagarajan, P. S. Akshay, S.
Abstract	Dynamic Bayesian Networks (DBNs) can serve as succinct models of large biochemical networks [19]. 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 and hence approximate methods are needed. The Factored Frontier algorithm (FF) is a simple and efficient approximate algorithm [25] that has been designed to meet this need. However the errors it incurs can be quite large. The earlier Boyen-Koller (BK) algorithm [3] can also incur significant errors. To address this, we present here a novel approximation algorithm called the Hybrid Factored Frontier (HFF) algorithm. HFF may be viewed as a parametrized version of FF. At each time slice, in addition to maintaining probability distributions over local states -as FF does- we also maintain explicitly the probabilities of a small 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 - computationally infeasible- 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 have validated the performance of our algorithm on large DBN models of biopathways. Each pathway has more than 30 species and the corresponding DBN has more than 3000 nodes. Comparisons with the performances of FF and BK show that HFF can be a useful and powerful approximation algorithm for analyzing DBN models of biopathways.
Starting Page	35
Ending Page	44
Page Count	10
File Format	PDF
ISBN	9781450308175
DOI	10.1145/2037509.2037516
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-09-21
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways

A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways

1a hybrid factored frontier algorithm for dynamic bayesian networks with a biopathways application.

A Hybrid Factored Frontier Algorithm for Dynamic Bayesian Network Models of Biopathways

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