Quantifying the inference power of a drug screen for predictive analysis

Noah Berlow, Saad Haider, Ranadip Pal, Charles Keller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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

Original languageEnglish
Title of host publication2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Proceedings
Pages49-52
Number of pages4
DOIs
StatePublished - 2013
Event2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Houston, TX, United States
Duration: Nov 17 2013Nov 19 2013

Publication series

NameProceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
ISSN (Print)2150-3001
ISSN (Electronic)2150-301X

Conference

Conference2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013
CountryUnited States
CityHouston, TX
Period11/17/1311/19/13

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  • Cite this

    Berlow, N., Haider, S., Pal, R., & Keller, C. (2013). Quantifying the inference power of a drug screen for predictive analysis. In 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Proceedings (pp. 49-52). [6735928] (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics). https://doi.org/10.1109/GENSIPS.2013.6735928