TY - GEN
T1 - Quantifying the inference power of a drug screen for predictive analysis
AU - Berlow, Noah
AU - Haider, Saad
AU - Pal, Ranadip
AU - Keller, Charles
PY - 2013
Y1 - 2013
N2 - A model for drug sensitivity prediction is often inferred from the response of a training drug screen. Quantifying the inference power of perturbations before experimentation will assist in selecting drugs screens with higher predictive power. In this article, we present a novel approach to quantify the inference power of a drug screen based on drug target profiles and biologically motivated monotonicity constraints. We have tested our algorithm on synthetically and experimentally generated datasets and the results illustrate the suitability of the proposed measure in estimating information gained from an experimental drug screen
AB - A model for drug sensitivity prediction is often inferred from the response of a training drug screen. Quantifying the inference power of perturbations before experimentation will assist in selecting drugs screens with higher predictive power. In this article, we present a novel approach to quantify the inference power of a drug screen based on drug target profiles and biologically motivated monotonicity constraints. We have tested our algorithm on synthetically and experimentally generated datasets and the results illustrate the suitability of the proposed measure in estimating information gained from an experimental drug screen
UR - http://www.scopus.com/inward/record.url?scp=84897729786&partnerID=8YFLogxK
U2 - 10.1109/GENSIPS.2013.6735928
DO - 10.1109/GENSIPS.2013.6735928
M3 - Conference contribution
AN - SCOPUS:84897729786
SN - 9781479934621
T3 - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
SP - 49
EP - 52
BT - 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Proceedings
T2 - 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013
Y2 - 17 November 2013 through 19 November 2013
ER -