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AI Disease Modeling Supports Precision Medicine for Cancer

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Posted on June 1st, 2021 by 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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