Application of Bayesian Network in Drug Discovery and Development Process
Arunkumar Chinnasamy (Bioinformatics Institute, Singapore), Sudhanshu Patwardhan (Bioinformatics Institute, Singapore) and Wing-Kin Sung (National University of Singapore, Singapore)
Copyright: © 2007
The end of the 20th century and the advent of the new millennium have brought in a true merger of sciences for the benefit of mankind. The biggest promise it holds is that of improving the quality of human life by the discovery of newer medicines and better cures for diseases such as cancer and heart disease. Pharmaceutical companies and academic institutions alike have not failed to deliver on part of the promise by bringing out technologies and products that have significantly decreased mortality and morbidity associated with these diseases. An increase in the scale and complexity of the technologies has made it increasingly important to develop intelligent tools to analyze their output, and numerous mathematical and statistical techniques have been explored and exploited to do exactly this. Bayesian networks (BN) and similar graphical models for multivariate analysis are being used for analyzing these data with great success. They have made possible a high resolution insight into disease mechanisms like never before. These insights into the biological processes of health and disease have helped identify the appropriate targets for drug discovery and aided in the process of bringing better drugs faster to the market for patients in need. This chapter briefly explains the application and contribution of Bayesian networks to the drug discovery and development process.