Researchers at the University of Warwick's Department of Statistics and Centre for Complexity Science have devised a new research tool that could help unpick the complex cell interactions that lead to cancer and also allow social scientists to mine social networking sites such as Facebook for useful insights.
An approach called "graphical models" can be used by researchers to gain an understanding of a range of systems with multiple interacting factors. These models use mathematical objects called graphs to describe and depict the probability of relationships between each of the components. When used to study molecular biology researchers may be interested in saying something about which molecules influence one another; in the social sciences researchers would use them to understand the relationships between various economic and demographic factors.
However gaining such information from a graphical model can be a very challenging exercise, because of the vast range of possible graphs needed for even a relatively small number of variables. For instance the relatively small network studied by the University of Warwick led team for this research paper had just 14 proteins which were implicated in the development of a form of cancer, but those 14 proteins had a vast number of combinations of possible mutual interactions.
Such tasks would be made much easier if the mathematical tools used to undertake the analysis could somehow embody all the current knowledge of what was likely, and or probable, in the networks they were analysing. Such a mathematical method could be viewed as mimicking how human researchers learn from data, in effect interpreting new information in light of what is already known.
The Warwick researchers led by Dr Sach Mukherjee of Warwick's Department of Statistics and Centre for Complexity Science have devised just such a method that embeds current knowledge in the mathematical analysis to cut through the va
|Contact: Dr. Sach Mukherjee|
University of Warwick