Big mechanisms are large, explanatory models of complicated systems in which interactions have important causal effects. A greater understanding of such mechanisms is of particular importance for research into cancer biology, since it is not fully understood how to stop cancer cells from growing faster than normal cells. Given that most cancer drugs are highly toxic, it is important to find drug combinations that are tailored to individual patients and their cancer’s genotypes, and which facilitate intervention at multiple points along a cancer pathway.
This project, one of a number funded by DARPA as part of their Big Mechanism programme, aims to address the issues introduced above by automating the process of intelligent, optimised drug discovery in cancer research. This will be achieved through the application of a number of different techniques:
- Text Mining (TM) techniques will be developed to locate, extract, interpret and assimilate relevant nuggets of information which are fragmented and scattered throughout the vast volumes of potentially relevant literature, in order to build up a detailed background knowledge about causal models of cancer mechanisms.
- Claims about cancer extracted by the TM analysis will be used to populate custom-designed ontologies to enable computational modelling and integration of information relating to cancer mechanisms and pathways.
- Probabilistic reasoning over these models will facilitate automated hypothesis generation to strategically extend the knowledge.
- A ‘Robot Scientist’ will perform experiments to test hypotheses and feed results back into the system