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
"I’m excited to help bring up the next generation of scientists to ask important questions of life."
Assistant Investigator Siva Sankari, Ph.D., joined Kaylee Peile, Director of Development at the Linda Hall Library, for the Library's Women in Stem series. Sankari discussed her lab's microbe research, why she joined the Institute, and her passion for diversity in STEM.
“What brought me to the Stowers Institute is the uniqueness and freedom we have to work on the aspects of science we’re passionate about. I’m super excited that I get to do what I love," Sankari said. "Every day, I get to research the questions I want to answer in science. I also get the support to train who I want to train. I’m excited to help bring up the next generation of scientists to ask important questions of life."
The Linda Hall Library's Women in STEM series focuses on the work and accomplishments of women in the fields of science, technology, engineering, and mathematics. The series aims to highlight and lift up the women making advancements in their STEM industries and their work in Kansas City and beyond.
"I want women and those in underrepresented groups to have the courage to express their passions and go for it,” Sankari said.
Watch the video above to hear the full interview.
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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