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The SVM-based classifier has been developed using 20 dimensions of vector, one for each amino acid.
An SVM-based classifier has been developed, using four dimensions of vector, one for each domain; this achieved 61.41% sensitivity, 90.65% specificity, 76.08% accuracy and 0.54 MCC.
In the hybrid approach, we combined 14 selected dipeptides with 5 PROSITE domain features and created total 19 dimensions of vector.
In the hybrid approach, we combined 18 selected dipeptides with 4 PROSITE domain features and created total 22 dimensions of vector (figure 8).
We generated a total of 405 dimensions of vector by using 400 features of dipeptide composition and 5 features of the selected five domains of PROSITE.
In the hybrid approach, we generated a total of 19 dimensions of vector by using 14 features of selected dipeptides and 5 features of PROSITE domains.
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Figure 15 depicts parallel processing of n dimensions of vectors p and q using DFEs.
A growing number of studies indicate ENM is a useful tool for understanding ecological and geographic dimensions of vectors and disease reservoirs [19] [27] and for identifying areas that may become affected under future climates, causing public health problems [28] [32].
In the sparse sampling signal model of RBSR, the dimension of vector x is still M×1.
The dimension of vector u is 2L F (L F + 2 N t.
Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm.
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