Genetic function approximation in the molecular pharmacology of cancer
Leming M. Shi, Yi Fan, Timothy G. Myers, John N. Weinstein · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
The National Cancer Institute's Developmental Therapeutics Program screens more than 10,000 compounds per year for their ability to inhibit growth of 60 human cancer cell lines. Using a combination of cross-validated backpropagation neural networks and multivariate statistical methods, we found that a compound's mechanism of action could be predicted with considerable accuracy solely on the basis of its pattern of growth inhibitory activity against the 60 cell lines (Weinstein, et al. 1992, 1997). Over the last several years, the developments, in terms of different mathematical approaches, led to formulation of a general "information-intensive" strategy for drug discovery that integrates data on a compounds's molecular structure, pattern of growth inhibitory activity, and possible molecular targets in the cell. Here we summarize our recent investigations of a new approach to the regression problem, "genetic function approximation".