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AI Disease Modeling Supports Precision Medicine for Cancer
Posted on June 1st, 2021 by Matthew Morton in Pharma R&D
Precision medicine has offered a lot of hope for the treatment of cancer, as it makes it possible to tackle the multi-faceted disease with a more personalized, patient-specific approach. Meanwhile, researchers are also becoming increasingly interested in applications for artificial intelligence in the development of drugs and therapies. Could AI and precision medicine work together in the fight against cancer?
A new collaboration between Elsevier and Sinergia consortium in search of drugs for pediatric cancer suggests that the answer is yes.
Using a knowledge graph to develop a disease model
The specific cancer that the team, led by biological modeling expert Dr. Anton Yuryev, has been focused on is a brain cancer called diffuse intrinsic pontine glioma (DIPG). Difficult to treat and usually yielding a poor prognosis, the National Cancer Institute describes DIPG as “a rare, fast-growing tumor that forms in cells called glial cells in a part of the brain stem called the pons,” noting that these gliomas tend to spread to nearby tissue and other parts of the brain stem.
In Dr. Yuryev’s project, OMICs data for patients with DIPG was analyzed using an Elsevier biology knowledge graph (discover more about knowledge graphs here) and software to develop a molecular disease model. The team then used the model to identify FDA-approved drugs inhibiting the disease mechanism.
What they have learned from the project so far is not only exciting as a look at how disease modeling can inform oncologists’ decisions in precision medicine, but the knowledge could also be applied to the treatment of other complex diseases.
Dive deeper in the webinar
To find out more about this fascinating project, watch Dr. Yuryev’s upcoming webinar, How AI disease modeling can support precision medicine for glioma and other cancers, on June 10. The webinar will discuss:
* Importing patient OMICs data, projecting it onto an Elsevier biology knowledge graph, and building a unified consensus disease model
* Finding and ranking drugs that could inhibit the disease mechanism, and how this drug selection was refined
* The mutations found in all DIPG patients and how common these are in other cancer types
* Audience Q&A
To sign up for the webinar, register here. Even if you aren’t available to watch at the scheduled date and time, you can still register now to later receive a link to the recorded webinar.
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