CyclinPred: a SVM-based method for predicting cyclin protein sequences.

ABSTRACT: Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training

SUBMITTER: Kalita MK 

PROVIDER: S-EPMC2435623 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature