Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

ABSTRACT:

Background

Enzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes. Automatically categorizing enzyme into the EC hierarchy is crucial to understand its specific molecular mechanism.

Results

In this paper, we introduce two key improvements in predicting enzyme function within the machine learning framework. One is to introduce the efficient sequence encoding methods for representing given proteins. The second one is to develop a structure-based prediction method with low computational complexity. In particular, we propose to use the conjoint triad feature (CTF) to represent the given protein sequenc

SUBMITTER: Wang YC 

PROVIDER: S-EPMC3121122 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature