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idse-HE: Hybrid embedding graph neural network for drug side effects prediction - ScienceDirect
Source: sciencedirect.com
Article summary:
1. A hybrid embedding graph neural network model (idse-HE) is proposed to jointly learn drug features from both the macroscopic biological network and the microscopic drug molecules.
2. The model deems the final representation of drugs and side effects as two implicit factors to reconstruct the original matrix and predicts potential side effects of drugs.
3. Experimental results indicate that idse-HE is superior to other advanced methods, with evidence confirming several real drug side effect pairs in the predicted results.
Article analysis:
The article provides a comprehensive overview of a new hybrid embedding graph neural network model (idse-HE) for predicting potential side effects of drugs. The article is well written and provides detailed information on how the model works, its performance, and its advantages over existing models. However, there are some points that could be improved upon in terms of trustworthiness and reliability.
First, while the article does provide evidence for some of its claims, it does not provide enough evidence to support all of its claims. For example, while it states that idse-HE is superior to other advanced methods, it does not provide any data or statistics to back up this claim. Additionally, while it mentions that evidence has been collected to confirm several real drug side effect pairs in the predicted results, it does not provide any details on these results or how they were verified.
Second, there is a lack of discussion about possible risks associated with using this model for predicting potential side effects of drugs. While the article does mention that unexpected side effects are one of the main reasons for failure in candidate drug trials, it does not discuss any potential risks associated with using this model or how these risks can be mitigated.
Finally, there is also a lack of discussion about alternative models or approaches for predicting potential side effects of drugs. While the article does mention some existing models and approaches used for this purpose, it does not explore any alternatives or counterarguments which could be used instead or in addition to idse-HE.
In conclusion, while this article provides an informative overview of idse-HE and its advantages over existing models for predicting potential side effects of drugs, there are still areas where more information could be provided in order to increase trustworthiness and reliability.