Summary of Gesubnet: Gene Interaction Inference For Disease Subtype Network Generation, by Ziwei Yang et al.
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
by Ziwei Yang, Zheng Chen, Xin Liu, Rikuto Kotoge, Peng Chen, Yasuko Matsubara, Yasushi Sakurai, Jimeng Sun
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed GeSubNet framework learns a unified representation that predicts gene interactions while distinguishing between different disease subtypes. It consists of three modules: a deep generative model for learning distinct disease subtypes, a graph neural network for capturing prior gene networks from knowledge databases, and an inference loss that leverages graph generation capabilities conditioned on patient separation loss to refine subtype-specific information in the learned representation. GeSubNet outperforms traditional methods by 30.6%, 21.0%, 20.1%, and 56.6% across four graph evaluation metrics averaged over four cancer datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GeSubNet is a new way to understand how genes work together to cause different types of diseases. Right now, it’s hard to figure out which genes are important for each disease because there isn’t enough information about how they interact with each other. GeSubNet helps by learning patterns in gene interactions and identifying the most important ones for each disease subtype. This could lead to new ways to diagnose and treat different types of cancer. |
Keywords
» Artificial intelligence » Generative model » Graph neural network » Inference
