Summary of Fast Polypharmacy Side Effect Prediction Using Tensor Factorisation, by Oliver Lloyd et al.
Fast Polypharmacy Side Effect Prediction Using Tensor Factorisation
by Oliver Lloyd, Yi Liu, Tom R. Gaunt
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM)
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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 This paper investigates the application of tensor factorization models in predicting adverse drug reactions from combinations. The authors aim to optimize these models for accurate prediction, as current laboratory-based methods are insufficient due to the combinatorial nature of the problem. While previous computational approaches have shown mixed results, the study focuses on evaluating the capabilities of tensor factorization models when properly optimized. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is trying to help doctors predict when a combination of medicines might cause bad side effects. Right now, doctors can only test each medicine one at a time in a lab, which isn’t enough because there are many possible combinations. The study looks at special computer models that try to figure out what might happen if different medicines are combined. These models have had mixed results so far, and the goal is to make them better at predicting when bad side effects will happen. |
