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Optimizing exact genetic linkage computations

Content Provider	ACM Digital Library
Author	Geiger, Dan Fishelson, Maáyan
Abstract	Genetic linkage analysis is a challenging application which requires Bayesian networks consisting of thousands of vertices. Consequently, computing the likelihood of data, which is needed for learning linkage parameters, using exact inference procedures calls for an extremely efficient implementation that carefully optimizes the order of conditioning and summation operations. In this paper we present the use of stochastic greedy algorithms for optimizing this order. Our algorithm has been incorporated into the newest version of superlink, which is currently the fastest genetic linkage program for exact likelihood computations in general pedigrees. We demonstrate an order of magnitude improvement in run times of likelihood computations using our new optimization algorithm, and hence enlarge the class of problems that can be handled effectively by exact computations.
Starting Page	114
Ending Page	121
Page Count	8
File Format	PDF
ISBN	1581136358
DOI	10.1145/640075.640089
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-04-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Greedy algorithms Probabilistic algorithms Superlink Dag models Bayesian networks Combinatorial optimization Genetic linkage analysis Treewidth
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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