Apoptosis protein is
a kind of protein with specific functions, play an important role in the growth
and homeostasis of organisms.
During apoptosis the
anti-apoptosis and pro-apoptosis plays a different role in the regulation of apoptosis.
If the inactivation of anti-apoptosis proteins or pro-apoptosis proteins, it
will lead to the occurrence of cancer and other diseases. So the classification
of anti-apoptotic proteins and pro-apoptotic proteins will help us understand
the pathogenic mechanism of the apoptosis proteins better. In this paper, a new
apoptosis proteins data set is built, and the two kinds of apoptosis in the new
data set are predicted by using the increment of diversity（ID）and
support vector machine（SVM）algorithm based on the structural and pseudo-amino
acid composition information. According to the biological and physicochemical
characteristics of apoptosis protein, we extracted several feature information
shown as follow: protein sequence information, amino acid hydrophilic-hydrophobic properties, protein block information, evolutionary, chemical shifts and protein
n-terminal sequence component information, and furthermore, the
impact of the single feature and multi-feature fusion models on predictive
results was analyzed. The results show that the selection of more useful
feature information for improving the success rate is very important factor. This proposed effective algorithm will be able to
predict the classification of anti-apoptotic and pro-apoptotic proteins, to further improve the predictive capability,
enhance prediction credibility, and to predict unknown function of apoptosis
proteins.