Summary of Graph Neural Networks For Protein-protein Interactions — a Short Survey, by Mingda Xu et al.
Graph Neural Networks for Protein-Protein Interactions – A Short Survey
by Mingda Xu, Peisheng Qian, Ziyuan Zhao, Zeng Zeng, Jianguo Chen, Weide Liu, Xulei Yang
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper reviews various graph-based methods for predicting protein-protein interactions (PPIs), which play crucial roles in biological processes. Graph Neural Networks (GNN) and Graph Convolutional Networks (GCN) are categorized as the first group, while Graph Attention Networks (GAT), Graph Auto-Encoders, and Graph-BERT comprise the second group. Each approach manages graph-structured PPI network data uniquely, highlighting their distinct methodologies and potential for future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to predict protein-protein interactions (PPIs), which are important in many biological processes. Scientists have developed special methods that use graphs to find these interactions. Graph-based models like GNN, GCN, GAT, Auto-Encoders, and BERT can help us better understand PPIs. The paper looks at different approaches, how they work, and what they could do in the future. |
Keywords
» Artificial intelligence » Attention » Bert » Gcn » Gnn
