From Berkeley Lab Computing Sciences
Did you know that the tools used for analyzing relationships between social network users or ranking web pages can also be extremely valuable for making sense of big science data? On a social network like Facebook, each user (person or organization) is represented as a node and the connections (relationships and interactions) between them are called edges. By analyzing these connections, researchers can learn a lot about each user—interests, hobbies, shopping habits, friends, etc.
In biology, similar graph-clustering algorithms can be used to understand the proteins that perform most of life’s functions. It is estimated that the human body alone contains about 100,000 different protein types, and almost all biological tasks—from digestion to immunity—occur when these microorganisms interact with each other. A better understanding of these networks could help researchers determine the effectiveness of a drug or identify potential treatments for a variety of diseases.