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.