Quantifying the inference power of a drug screen for predictive analysis
- Resource Type
- Conference
- Authors
- Berlow, Noah; Haider, Saad; Pal, Ranadip; Keller, Charles
- Source
- 2013 IEEE International Workshop on Genomic Signal Processing and Statistics Genomic Signal Processing and Statistics (GENSIPS), 2013 IEEE International Workshop on. :49-52 Nov, 2013
- Subject
- Bioengineering
Signal Processing and Analysis
Drugs
Sensitivity
Vectors
Tumors
Power measurement
Inference algorithms
Predictive models
- Language
- ISSN
- 2150-3001
2150-301X
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