.png){kind=icon}
返回
Learning protein language contrastive models with multi-knowledge representation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2024.107580.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Protein representation learning plays a crucial role in obtaining a comprehensive understanding of biological regulatory mechanisms and in developing proteins and drugs for therapeutic purposes. However, labeled proteins, such as sequenced and functionally annotated data, are incomplete and few. Thus, contrastive learning has emerged as the preferred technique for learning meaningful representations from unlabeled data samples. In addition, at present, natural proteins cannot be fully described by extracting protein knowledge from a single domain. Therefore, Pro-CoRL, a protein contrastive models framework based on multi-knowledge representation learning, was proposed in this study. In particular, Pro-CoRL smooths the objective function using convex approximation, thereby improving the stability of training. Extensive experiments on predicting protein-protein interaction types and clustering protein families have confirmed the high accuracy and robustness of Pro-CoRL.
Keyword:
Protein representation learning
Contrastive learning
Multi-knowledge embeddings
Convex approximation
期刊
F
IF:
6.1
论文数:
6.9K
被引数:
2.3W
机构
引用论文
Assessment of community efforts to advance network-based prediction of protein-protein interactions.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏分享收藏分享收藏D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactionsCELL SYSTEMS
OAAI
CELL SYSTEMSIF7.7
OAAI分享收藏
查看更多内容