Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework.

ABSTRACT: Enzyme commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab initio computational approaches were proposed to predict EC numbers for given input protein sequences. However, the prediction performance (accuracy, recall, and precision), usability, and efficiency of existing methods decreased seriously when dealing with recently discovered proteins, thus still having much room to be improved. Here, we report HDMLF, a hierarchical dual-core multitask learning framework for accurately predicting EC numbers based on novel deep learning techniques. HDMLF is composed of an embedding core and a learning core; the embedding core adopts the latest protei

SUBMITTER: Shi Z 

PROVIDER: S-EPMC10232324 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature