Network embeddings for modeling polypharmacy and drug-drug interactions

author: Marinka Žitnik, Computer Science Department, Stanford University
published: June 28, 2019,   recorded: May 2019,   views: 74
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

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.

See Also:

Download slides icon Download slides: icgeb_zitnik_network_embeddings_01.pdf (18.0 MB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 fredluis, October 9, 2019 at 7:04 a.m.:

It's really great to post my comments on such a blog. I would like to appreciate the great work done by the web master and would like to tell everyone that they should post their interesting comments and should make this blog interesting. Once again I would like to say keep it up to blog owner!!!! https://www.metrolandscaperscfl.com

Write your own review or comment:

make sure you have javascript enabled or clear this field: