.png){kind=icon}
返回
SMENET: A Multi-View Semantic Model for Multi-Level Enzyme Function Prediction
.png){kind=icon}
PRE
AI
DOI：10.1109/TCBBIO.2025.3644035.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Comprehending biological reproduction and cellular metabolism is facilitated by the Enzyme Commission, which matches protein sequences to the biochemical reactions they catalyse through EC numbers. In recent years, several methods have been proposed for predicting enzyme function. However, these methods still encounter challenges. Firstly, traditional methods for manually designing enzyme features are complex and cumbersome, lacking an effective generalized method for embedding enzyme sequences. Secondly, the distribution gap between different enzymes is significant, which resulting in existing methods struggling to predict multilevel enzyme functions. Thirdly, traditional enzyme function prediction models only extract single view feature of enzyme, so there is still room for further improving the ability of these models to extract enzyme data. To address these challenges, a new multilevel enzyme function prediction model (SMENET) based on multi-view semantics is proposed. This method uses protein large language model to extract semantic information. Subsequently, this semantic information is fed into multiple information extraction network modules, followed by using Biologic Sematic Attention to integrate these views’ information. Finally, a multi-view adaptive fusion network is designed to extract the best common representation between multiple semantic views. Extensive experiments were conducted on multiple datasets to validate the effectiveness of SMENET.
Keyword:
Multi-level enzyme function prediction
multi-view learning
attention mechanism
protein sequence embedding
deep learning
large language model
期刊
I
IF:
0
论文数:
151
被引数:
0
机构
引用论文
From Beginning to BEGANing: Role of Adversarial Learning in Reshaping Generative Models.png)
Electronics
ElectronicsIF0
PREAI
分享收藏A Deep Learning Framework for Gene Ontology Annotations With Sequence- and Network-Based Information基于序列和网络信息的基因本体注释深度学习框架
OAAI
OAAI分享收藏A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications生成对抗网络研究综述: 算法、理论与应用
OAAI
OAAI分享收藏MVDINET: A Novel Multi-Level Enzyme Function Predictor With Multi-View Deep Interactive Learning
PREAI
分享收藏分享收藏分享收藏
查看更多内容