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In early 2001, Ding and Dubchak built the 27-class folds dataset and started research on the identification of 27-class protein folds with multiple feature groups.
In 2001, Ding and Dubchak [ 11] built the 27-class folds dataset and used multiple feature groups as parameters, including amino acid composition, predicted secondary structure, hydrophobicity, Van der Waals volume, polarity, and polarizability; then they proposed support vector machines and neural networks methods for the identification of 27-class protein folds.
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The AUC of the fully adjusted multiple models within the feature groups ranged from 0.67 to 0.74 (again, moment-based and statistical features, respectively).
These feature groups can all indicate whether an SNV is functional or not, and hence we use a classifier based on multiple kernel learning (MKL).
This supports our choice of using a multi-dimensional clustering tool, which can not only take into account multiple features but can also identify groups of emails that are linked by varying sets of commonalities (i.e., more volatile features).
Therefore, when the multiple real-value K-nearest neighbour classifier is fused by feature grouping, the performance of the fusion will be better than the single classifier.
Each axis is described by multiple features.
Multiple features of each object were quantified.
The Eastern Conference features groups of concentrated stars.
The proposed gene-level analysis integrating multiple cortisol features across multiple ethnic groups provides new insights into the gene-cortisol association.
Note that some systems may have features belonging to multiple groups and that within each group, we list systems in reverse chronological order.
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