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Previously we developed SPMap, which predicts protein function based on subsequence feature space mapping.
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By contrast, our arguments are based on subsequence techniques from Banach space theory.
The protein sequence that is more than 600 residues is split into subsequences to investigate the effectiveness of the protein domain prediction based on subsequence.
The combiner combines the selected subset of features based on feature class and feature-feature mutual information.
Data set would be processed based on classification feature.
Then, IFS method is applied based on mRMR feature table.
Results: We developed a fast homology search method based on database subsequence clustering, and implemented it as GHOSTZ.
This general characterization of strings based on their subsequence composition closely resembles some of the information theory problems, and is tightly related with the compression of strings.
Feature-based tracking algorithms could be break down into the three categories: algorithms based on global features, algorithms based on local features, and algorithms based on dependency graph.
Algorithms based on features usually extract significant and stable features.
The feature vector is then generated based on these features.
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