EC2Vec: A Machine Learning Method to Embed Enzyme Commission (EC) Numbers into Vector Representations.

ABSTRACT: Enzyme commission (EC) numbers play a vital role in classifying enzymes and understanding their functions in enzyme-related research. Although accurate and informative encoding of EC numbers is essential for enhancing the effectiveness of machine learning applications, simple EC encoding approaches suffer from limitations such as false numerical order and high sparsity. To address these issues, we developed EC2Vec, a multimodal autoencoder that preserves the categorical nature of EC numbers and leverages their hierarchical relationships, resulting in more meaningful and informative representations. EC2Vec encodes each digit of the EC number as a categorical token and then processes these embeddings through a 1D convolutional layer to capture their relationships. Comprehensive benchmarking

SUBMITTER: Liu M 

PROVIDER: S-EPMC11898066 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature