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Additionally, we propose a pseudo-relevance feedback algorithm based on the mixture modeling of the Dirichlet compound multinomial distribution to further improve retrieval accuracy.
Gaussian mixture modeling of distances between binding sites and motifs was performed with R package mclust (Fraley & Raftery, 2002).
Results: We here introduce a new method for taxonomic profiling based on mixture modeling of the overall oligonucleotide distribution of a sample.
Our novel taxonomic profiling approach 'Taxy-Pro' is based on mixture modeling of the protein domain frequencies in a metagenome as described in the following.
For mixture modeling of protein signatures, we aim to reconstruct the overall Pfam domain frequencies of a metagenome by a linear combination of genomic reference signatures.
Results: First, we estimated the prior variance of a gene expression by pooling variances obtained from mixture modeling of large samples in the public microarray database.
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The Mixture model of ANSYS Fluent has been used.
However, mixture models of some suitable lifetime distributions, have the potential to highlight such interesting feature.
Another identification problem is associated with the use of a mixture model of too many components (overfitting).
Note that is the Gaussian mixture model of the overall sensitivity distribution in (8), which can be regarded as the reliability of the sensitivity.
The Gaussian mixture models of objects and background were then built under the Bayesian framework and the prior information on object shapes was introduced.
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