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A number of different methods for inferring gene regulatory networks (GRNs) from high-throughput data have been proposed [ 14- 20].
Different methods for inferring gene conversion were typically concordant; one illustration of this is shown in Figure 4, in which two significant events affecting Tb62.2, 3 and 4 were identified by both SSC and RETICULATE, although a third was not detected by the latter.
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There are many different methods of inferring the structure of such regulatory networks in silico.
MRNET, CLR and ARACNE are different methods for using the mutual information to infer a weighted adjacency matrix between genes.
Figure 7 shows the sire's blocking structure that was inferred by the different methods for a phased half-sib family with 23 individuals from the real dataset.
Methods for inferring instrumental constants and coordinate systems for the different events, by using the images of stars, are described in this section.
As only relative gene expression levels can be inferred reliably, studies requiring measures of absolute gene expression are best advised to rely on different methods for expression profiling.
Different methods have been devised for dealing with gapped sites during phylogenetic analysis, ranging from ignoring the gapped sites from the alignment to inferring or differentially coding the state at each gapped site, using a number of different methods (for a list of methods, see [ 4- 6]).
Similarly, methods for inferring available bandwidth in dynamic mobile networks and using it to drive different application optimizations will be presented.
To compare the classification performance of different inference methods, we also repeated the previous experiments using the CORG, PCA, mean, and median methods for inferring the pathway activities.
There are many different methods for teaching math.
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