ENZYMAP: exploiting protein annotation for modeling and predicting EC number changes in UniProt/Swiss-Prot.

ABSTRACT: The volume and diversity of biological data are increasing at very high rates. Vast amounts of protein sequences and structures, protein and genetic interactions and phenotype studies have been produced. The majority of data generated by high-throughput devices is automatically annotated because manually annotating them is not possible. Thus, efficient and precise automatic annotation methods are required to ensure the quality and reliability of both the biological data and associated annotations. We proposed ENZYMatic Annotation Predictor (ENZYMAP), a technique to characterize and predict EC number changes based on annotations from UniProt/Swiss-Prot using a supervised learning approach. We evaluated ENZYMAP experimentally, using test data sets from both UniProt/Swiss-Prot and UniProt/TrE

SUBMITTER: Silveira Sde A 

PROVIDER: S-EPMC3929618 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature