In The News

31 August 2026
The Precision Revolution
From Kansas City Magazine, Kansas City’s hospitals and researchers are harnessing AI, genomics and precision medicine to create treatments tailored to each patient
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News
Investigator Jennifer Gerton, PhD, has been awarded The University of Kansas Cancer Center’s 2022 Director's Award for Basic Science.

The Stowers Institute is proud to announce that Investigator Jennifer Gerton, PhD, has been awarded The University of Kansas Cancer Center’s 2022 Director's Award for Basic Science.
The annual Director's Awards program recognizes individuals who have made significant contributions to KUCC’s mission of reducing the burden of cancer in the Kansas City region.

Jennifer Gerton, PhD with Dr. Roy Jensen from the University of Kansas Medical Center.
Gerton participates in a research advocacy initiative at the Cancer Center called PIVOT, Patient and Investigator Voices Organizing Together, to help patients understand how genetics play a role in their disease.
Through PIVOT, Gerton was teamed up with a breast cancer patient. The goal is to give the patient a better understanding of genetics and give the scientist a concrete example of how their foundational research can impact human health.
To learn more about Gerton’s work with the Cancer Center click here.
Recipients were announced at The University of Kansas Cancer Center’s annual Research Week, which celebrates research led by cancer center members.
In The News

31 August 2026
From Kansas City Magazine, Kansas City’s hospitals and researchers are harnessing AI, genomics and precision medicine to create treatments tailored to each patient
Read Article
In The News

26 August 2026
From InnotechInsider, Stowers is highlighted among leading research institutes integrating AI directly into biological research.
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News

25 August 2026
Reflecting on the 2026 Stowers Summer Scholars Program
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Press Release

25 August 2026
A team from the Zeitlinger Lab developed PISA, a new method for visualizing what deep-learning models learn from DNA at single-base resolution. By separating experimental bias from biological signal, the team was able to train a more focused model that revealed previously hidden DNA sequence features linked to nucleosome positioning and the genome’s larger three-dimensional organization. The work also shows how model interpretation can guide new experiments and help biologists move from AI prediction toward biological mechanism.
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