Network Completion Using Dynamic Programming and Least-Squares Fitting
We consider the problem of network completion, which is to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We employ here a certain type of differential equations as gene regulation rules in a genetic network, gene...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1100/2012/957620 |
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| Summary: | We consider the problem of network completion, which is to make
the minimum amount of modifications to a given network so that the resulting network
is most consistent with the observed data. We employ here a certain type of differential
equations as gene regulation rules in a genetic network, gene expression time series data
as observed data, and deletions and additions of edges as basic modification operations.
In addition, we assume that the numbers of deleted and added edges are specified. For
this problem, we present a novel method using dynamic programming and least-squares
fitting and show that it outputs a network with the minimum sum squared error in
polynomial time if the maximum indegree of the network is bounded by a constant. We
also perform computational experiments using both artificially generated and real gene
expression time series data. |
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| ISSN: | 1537-744X |