Summary of Multiple Kronecker Rls Fusion-based Link Propagation For Drug-side Effect Prediction, by Yuqing Qian et al.
Multiple Kronecker RLS fusion-based link propagation for drug-side effect prediction
by Yuqing Qian, Ziyu Zheng, Prayag Tiwari, Yijie Ding, Quan Zou
First submitted to arxiv on: 27 Jun 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 A novel data-driven method, Multiple Kronecker RLS fusion-based link propagation (MKronRLSF-LP), is proposed for predicting drug-side effects. This multi-view approach extends the Kron-RLS by incorporating consensus partitions and multiple graph Laplacian constraints in a link prediction problem. The authors apply this methodology to various drug-side effect datasets, demonstrating its effectiveness and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict potential side effects of medicines is developed. Researchers use data-driven methods like MKronRLSF-LP to understand the risks associated with medications. This method looks at different types of information about drugs and their side effects. It works well on real-world datasets, showing that it’s a good tool for making predictions. |
