A novel approach for tumor sensitivity prediction and combination therapy design for targeted drugs

Noah Berlow; Ranadip Pal

A novel approach for tumor sensitivity prediction and combination therapy design for targeted drugs

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Drugs that target specific kinases are becoming common in cancer research. Cancer-related kinases are the paradigm of molecularly-targeted therapies, and a cornerstone of Personalized Cancer Therapy. Here, we present an approach to generate abstract circuit representations of cancer pathways from drug tumor sensitivity data of a cell line and utilize them to predict the sensitivities of a new drug given the kinase inhibitors of the drug and to design and measure the effectiveness of drug combination therapies.

Original language	English
Title of host publication	Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
Pages	36-37
Number of pages	2
State	Published - Dec 1 2011
Event	2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 - San Antonio, TX, United States Duration: Dec 4 2011 → Dec 6 2011

Publication series

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

Conference

Conference	2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
Country	United States
City	San Antonio, TX
Period	12/4/11 → 12/6/11

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Berlow, N., & Pal, R. (2011). A novel approach for tumor sensitivity prediction and combination therapy design for targeted drugs. In Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 (pp. 36-37). [6169435] (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics).

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abstract = "Drugs that target specific kinases are becoming common in cancer research. Cancer-related kinases are the paradigm of molecularly-targeted therapies, and a cornerstone of Personalized Cancer Therapy. Here, we present an approach to generate abstract circuit representations of cancer pathways from drug tumor sensitivity data of a cell line and utilize them to predict the sensitivities of a new drug given the kinase inhibitors of the drug and to design and measure the effectiveness of drug combination therapies.",

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Berlow, N & Pal, R 2011, A novel approach for tumor sensitivity prediction and combination therapy design for targeted drugs. in Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11., 6169435, Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics, pp. 36-37, 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11, San Antonio, TX, United States, 12/4/11.

A novel approach for tumor sensitivity prediction and combination therapy design for targeted drugs. / Berlow, Noah; Pal, Ranadip.

Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11. 2011. p. 36-37 6169435 (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Drugs that target specific kinases are becoming common in cancer research. Cancer-related kinases are the paradigm of molecularly-targeted therapies, and a cornerstone of Personalized Cancer Therapy. Here, we present an approach to generate abstract circuit representations of cancer pathways from drug tumor sensitivity data of a cell line and utilize them to predict the sensitivities of a new drug given the kinase inhibitors of the drug and to design and measure the effectiveness of drug combination therapies.

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M3 - Conference contribution

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T3 - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics

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BT - Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11

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