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

1 Citation (Scopus)

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 (US)
Title of host publicationProceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
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

Other

Other2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013
CountryUnited States
CityHouston, TX
Period11/17/1311/19/13

Fingerprint

Pharmaceutical Preparations
Predictive analytics
Datasets

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Computational Theory and Mathematics
  • Signal Processing
  • Biomedical Engineering

Cite this

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

Quantifying the inference power of a drug screen for predictive analysis. / Berlow, Noah; Haider, Saad; Pal, Ranadip; Keller, Charles.

Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics. 2013. p. 49-52 6735928.

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

Berlow, N, Haider, S, Pal, R & Keller, C 2013, Quantifying the inference power of a drug screen for predictive analysis. in Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics., 6735928, pp. 49-52, 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013, Houston, TX, United States, 11/17/13. https://doi.org/10.1109/GENSIPS.2013.6735928
Berlow N, Haider S, Pal R, Keller C. Quantifying the inference power of a drug screen for predictive analysis. In Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics. 2013. p. 49-52. 6735928 https://doi.org/10.1109/GENSIPS.2013.6735928
Berlow, Noah ; Haider, Saad ; Pal, Ranadip ; Keller, Charles. / Quantifying the inference power of a drug screen for predictive analysis. Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics. 2013. pp. 49-52
@inproceedings{01711fccca2c417b9122ef0dc3aaef99,
title = "Quantifying the inference power of a drug screen for predictive analysis",
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",
author = "Noah Berlow and Saad Haider and Ranadip Pal and Charles Keller",
year = "2013",
doi = "10.1109/GENSIPS.2013.6735928",
language = "English (US)",
isbn = "9781479934621",
pages = "49--52",
booktitle = "Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics",

}

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

UR - http://www.scopus.com/inward/citedby.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

SP - 49

EP - 52

BT - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics

ER -