Improved prediction of malaria degradomes by supervised learning with SVM and profile kernel.

ABSTRACT: The spread of drug resistance through malaria parasite populations calls for the development of new therapeutic strategies. However, the seemingly promising genomics-driven target identification paradigm is hampered by the weak annotation coverage. To identify potentially important yet uncharacterized proteins, we apply support vector machines using profile kernels, a supervised discriminative machine learning technique for remote homology detection, as a complement to the traditional alignment based algorithms. In this study, we focus on the prediction of proteases, which have long been considered attractive drug targets because of their indispensable roles in parasite development and infection. Our analysis demonstrates that an abundant and complex repertoire is conserved in five Plasmod

SUBMITTER: Kuang R 

PROVIDER: S-EPMC2721224 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature