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原文連結
論文資訊
- 類型：已發表論文
- 日期：2023-06-17
摘要
Drugs can impact the gut microbiome. Here, Algavi and Borenstein developed a machine-learning framework that successfully predicts the impact of thousands of drugs on hundreds of gut microbes, explaining drug-induced dysbiosis and side effects. Many medications can negatively impact the 細菌 residing in our gut, depleting beneficial 物種, and causing adverse effects. To guide personalized pharmaceutical treatment, a comprehensive understanding of the impact of various drugs on the gut microbiome is needed, yet, to date, experimentally challenging to obtain. Towards this end, we develop a data-driven approach, integrating 資訊 about the chemical properties of each drug and the genomic content of each microbe, to systematically predict drug-microbiome interactions. We show that this framework succ
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