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GloEC: a hierarchical-aware global model for predicting enzyme function.
Huang, Yiran; Lin, Yufu; Lan, Wei; Huang, Cuiyu; Zhong, Cheng.
Afiliación
  • Huang Y; School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China.
  • Lin Y; Key Laboratory of Parallel, Distributed and Intelligent Computing in Guangxi Universities and Colleges, Guangxi University, Nanning 530004, China.
  • Lan W; Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China.
  • Huang C; School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China.
  • Zhong C; School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China.
Brief Bioinform ; 25(5)2024 Jul 25.
Article en En | MEDLINE | ID: mdl-39073830
ABSTRACT
The annotation of enzyme function is a fundamental challenge in industrial biotechnology and pathologies. Numerous computational methods have been proposed to predict enzyme function by annotating enzyme labels with Enzyme Commission number. However, the existing methods face difficulties in modelling the hierarchical structure of enzyme label in a global view. Moreover, they haven't gone entirely to leverage the mutual interactions between different levels of enzyme label. In this paper, we formulate the hierarchy of enzyme label as a directed enzyme graph and propose a hierarchy-GCN (Graph Convolutional Network) encoder to globally model enzyme label dependency on the enzyme graph. Based on the enzyme hierarchy encoder, we develop an end-to-end hierarchical-aware global model named GloEC to predict enzyme function. GloEC learns hierarchical-aware enzyme label embeddings via the hierarchy-GCN encoder and conducts deductive fusion of label-aware enzyme features to predict enzyme labels. Meanwhile, our hierarchy-GCN encoder is designed to bidirectionally compute to investigate the enzyme label correlation information in both bottom-up and top-down manners, which has not been explored in enzyme function prediction. Comparative experiments on three benchmark datasets show that GloEC achieves better predictive performance as compared to the existing methods. The case studies also demonstrate that GloEC is capable of effectively predicting the function of isoenzyme. GloEC is available at https//github.com/hyr0771/GloEC.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Biología Computacional / Enzimas Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Biología Computacional / Enzimas Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article