TY - GEN
T1 - Genetic function approximation in the molecular pharmacology of cancer
AU - Shi, Leming M.
AU - Fan, Yi
AU - Myers, Timothy G.
AU - Weinstein, John N.
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 1997
Y1 - 1997
N2 - 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".
AB - 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".
UR - http://www.scopus.com/inward/record.url?scp=0030685509&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0030685509&partnerID=8YFLogxK
U2 - 10.1109/ICNN.1997.614679
DO - 10.1109/ICNN.1997.614679
M3 - Conference contribution
AN - SCOPUS:0030685509
SN - 0780341228
SN - 9780780341227
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 2490
EP - 2493
BT - 1997 IEEE International Conference on Neural Networks, ICNN 1997
T2 - 1997 IEEE International Conference on Neural Networks, ICNN 1997
Y2 - 9 June 1997 through 12 June 1997
ER -