TY - GEN
T1 - Analysis of multivariate drug sensitivity dependence structure using copulas
AU - Haider, Saad
AU - Pal, Ranadip
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/5
Y1 - 2014/2/5
N2 - Modeling sensitivity to anti-cancer drugs is a significant challenge in the area of systems medicine. Majority of current approaches generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. In this article, we approach the problem of modeling the relationship between different drugs using probabilistic concept of copulas and generate the multivariate distribution of the drugs based on the marginal distributions of individual models and the estimated copula. We first illustrate using drug sensitivity databases that specific forms of copulas can be suitable for modeling the multivariate distribution of drug sensitivities. Subsequently, we show that parametric copulas estimated from training data can be utilized to increase the conditional sensitivity prediction accuracy of testing data as compared to prediction assuming independence between drug sensitivities.
AB - Modeling sensitivity to anti-cancer drugs is a significant challenge in the area of systems medicine. Majority of current approaches generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. In this article, we approach the problem of modeling the relationship between different drugs using probabilistic concept of copulas and generate the multivariate distribution of the drugs based on the marginal distributions of individual models and the estimated copula. We first illustrate using drug sensitivity databases that specific forms of copulas can be suitable for modeling the multivariate distribution of drug sensitivities. Subsequently, we show that parametric copulas estimated from training data can be utilized to increase the conditional sensitivity prediction accuracy of testing data as compared to prediction assuming independence between drug sensitivities.
UR - http://www.scopus.com/inward/record.url?scp=84949928692&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2014.7032345
DO - 10.1109/GlobalSIP.2014.7032345
M3 - Conference contribution
AN - SCOPUS:84949928692
T3 - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
SP - 1352
EP - 1355
BT - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 December 2014 through 5 December 2014
ER -