Summary of New Methods For Drug Synergy Prediction: a Mini-review, by Fatemeh Abbasi and Juho Rousu
New methods for drug synergy prediction: a mini-review
by Fatemeh Abbasi, Juho Rousu
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
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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 mini-review explores the rapidly advancing field of predicting drug combination synergy through high-throughput combinatorial screens. Since 2021, over thirty original machine learning methods have been published, with a majority relying on deep learning techniques. The review aims to unify these papers by highlighting core technologies, data sources, input data types, and evaluation protocols. Key findings include the accurate prediction of synergy scenarios involving known drugs or cell lines, while those involving new drugs or cell lines still struggle to achieve accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper reviews new methods for predicting drug combination synergy. It looks at how machine learning is being used in this area. Over 30 papers have been published since 2021, with most using deep learning. The review shows that the best methods work well when predicting combinations of known drugs or cell lines. However, predicting new drug combinations still has some challenges. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
