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We perform computational experiments, and the results suggest that the CRF-based method using mutual information and labels with the lasso is useful.
The results suggest that our CRF-based method using mutual information and labels with the lasso is useful for further improving the performance, especially provided that the features of CRF are successfully reduced by the lasso approach.
Leakage signals are decomposed into mode components by VMD, and an adaptive selection method using mutual information is proposed to process these mode components and obtain the sensitive components closely related to the leak.
A hierarchical clustering method using mutual information between VSiftVar and VDisGene was used to detect relative closeness patterns of subpopulations in the 1000 Genomes Project data and diseases.
All together, these results suggest that the CRF-based method using mutual information and labels representing kinds of amino acids and bases with the lasso is very useful for further improving the prediction performance.
From the results, it is seen that the CRF-based method using mutual information and labels representing kinds of amino acids and bases with the lasso is very useful.
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To overcome the limitations, we introduced an ensemble feature selection method uses mutual information to select an optimal subset of features.
We suggest it's because all these methods use mutual information to represent the predictive power, and the traditional mutual information of mRMR is the most robust one to estimate feature redundancy in our experiments.
In this paper, we propose a novel hybrid imputation method using recursive mutual strategy, called RMI.
A couple of recent methods predict contact map by using mutual information, taking into consideration contact correlation and enforcing a sparsity restraint, but these methods demand for a very large number of sequence homologs for the protein under consideration and the resultant contact map may be still physically infeasible.
This method, which uses mutual information as the statistical measure, was previously applied successfully to the study of the PB2 polymerase [12].
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