BGU Cyber and Biology Team Up For New AI Algorithm
BGU Cyber and Biology Team Up For New AI Algorithm
April 24, 2025
Homeland & Cyber Security, Medical Research
Dr. Esti Yeger-Lotem, left, and Dr. Michael Fire of Ben-Gurion University of the Negev.
The Times of Israel – In an intriguing study, a Ben-Gurion University of the Negev (BGU) cybersecurity researcher who analyzes fraud on social networks joined forces with a team of BGU biologists to develop an AI machine-learning system to recognize abnormal activity in protein networks inside the human body.
Their innovative method, weighted graph anomalous node detection (WGAND), uses an algorithm that uncovers suspicious behavior in social networks such as LinkedIn or Instagram to discover anomalous behavior in networks of proteins inside cells.
“It’s exciting to see how bringing together expertise from cybersecurity can lead to breakthroughs in understanding human biology,” said Dr. Michael Fire, Assistant Professor in the Software and Information Systems Engineering Department at the University, who worked with lead researcher Prof. Esti Yeger-Lotem, in the Department of Clinical Biochemistry and Pharmacology, Dr. Juman Jubran and Dr. Dima Kagan.
Speaking to The Times of Israel by telephone, Prof. Fire said he and Prof. Yeger-Lotem “are on opposite ends of the BGU campus” and hadn’t met until the university announced it was offering grants for joint research projects.
“There’s a strong effort to encourage interdisciplinary collaboration, including grants for researchers who work together across faculties,” said Fire. “I work with people from other fields because AI has become an integral part of many different domains.”
This is where Prof. Yeger-Lotem’s work comes in.
In her lab, she develops and applies novel computational approaches in network biology, studying how proteins, genes, and other molecules communicate, and treating them as if they belong to a large social network inside the human body.
The same algorithms that uncover irregularities in social networks can be applied to atypical behavior in the networks of proteins.
“What is really cool about our method is that it is a generic algorithm,” Prof. Yeger-Lotem explained. “We can use it for predicting interesting protein behaviors, and in the same way, we can predict fake profiles or changes in a medical or transportation network.”
Read more on The Times of Israel>>
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