图 书 馆
多党派和药物相互作用建模的网络嵌入Network embeddings for modeling polypharmacy and drug-drug interactions |
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| 课程网址: | http://videolectures.net/icgeb_zitnik_network_embeddings/ |
| 主讲教师: | Marinka Žitnik |
| 开课单位: | 斯坦福大学计算机科学系 |
| 开课时间: | 2019-09-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Polypharmacy, the use of drug combinations, is common to treat patients with complex or co-existing diseases. However, a major consequence of polypharmacy is a high risk of adverse side effects, which emerge because of drug-drug interactions, in which activity of one drug changes if taken with another drug. Furthermore, polypharmacy is recognized as an increasingly serious problem in the health care system affecting nearly 15% of the U.S. population and costing more than $177 billion a year in the U.S. alone in treating side effects. In this talk, I will describe the methodology for large-scale predictive modeling of polypharmacy. We start by capturing molecular, drug, and patient data for all drugs prescribed in the U.S. These data are represented with a massively multimodal network of protein-protein interactions, drug-protein target interactions, and polypharmacy side effects. I will then describe a network embedding approach that embeds nodes in such multimodal networks into optimized low-dimensional vector spaces. Here, I will outline key advancements in learning embeddings for networks, with an emphasis on fundamentally new opportunities in computational biology enabled by these advancements. Finally, I will show how we can use the approach to, for the first time, predict safety and side effects of drug combinations and how we can validate predictions in the clinic using real patient data. |
| 关 键 词: | 多党派和药物相互作用; 数据科学; 建模的网络嵌入; 预测药物组合的安全性和副作用; 网络嵌入方法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-10-14：cyh |
| 最后编审: | 2022-10-14：cyh |
| 阅读次数: | 32 |