By Diana Bletter

Ben-Gurion University scientists say their innovative algorithm, WGAND, can identify rogue protein behavior the same way investigators uncover suspicious social network patterns

In an intriguing study, a Ben-Gurion University of the Negev cybersecurity researcher who analyzes fraud on social networks joined forces with a team of BGU biologists to develop a 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.

The researchers said WGAND enabled them to identify proteins associated with brain disorders and heart conditions, as well as those involved in critical biological processes, like neuron signaling in the brain and muscle contraction in the heart.

“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 Dr. Esti Yeger-Lotem, associate professor in the Department of Clinical Biochemistry & Pharmacology, Dr. Juman Jubran and Dr. Dima Kagan.