nction constructed for the factor Xa protease cleavage data [Yang,

06], where the peptides were encoded using the binary encoding

. Figure 3.33(b) and Figure 3.33(c) show the ROC curves of two

dels of the sigmoid and polynomial kernels constructed for the

ncoded factor Xa data. They show similar performance, but the

del using the polynomial kernel seems the best.

r than using the binary encoding approach for the amino acids, a

matrix can be used for transforming an amino acid space to a

ace. Having understood the powerfulness of the support vector

the bio-basis function was used as a kernel function for the

vector machine, which was named as the bio-support vector

(bio-SVM) [Yang and Chou, 2004b]. Figure 3.34 shows the ROC

the bio-SVM model constructed for the factor Xa protease

data, in which the Dayhoff mutation matrix [Dayhoff and

, 1978] was used to generate the bio-support vectors.

The ROC curve of the Dayhoff kernel bio-SVM for the factor Xa protease

ata. The AUC was 0.952.

e relevance vector machine algorithm

vance vector machine (RVM) algorithm [Tipping, 2001]

y employs the Gaussian kernel function, where ߚ is a smoothing

r for the Gaussian function, y is a vector of data labels, w is a