A computational framework for predicting drug Mode-of-Action

Chifeng Ma; Yufei Huang; Hung I. Chen; Yidong Chen

doi:10.1109/BIBMW.2010.5703831

A computational framework for predicting drug Mode-of-Action

Chifeng Ma, Yufei Huang, Hung I. Chen, Yidong Chen

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

Abstract

The problem of large scale computational drug screening is considered in this paper. A computational framework is developed for microarray profile of drug treatment from the Connectivity Map (cMap) project. The framework is proposed to address the deficiency of cMap prediction. This scheme includes construction of drug Mode-of-Actions (MoA) and subsequently Mode-of-Action (MoA) network, or MoANet and a MoA prediction algorithm based on MoAs. The scheme is tested on the cMap data and an independent data set. The result shows significant improvement over cMap.

Original language	English (US)
Title of host publication	2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Pages	374-379
Number of pages	6
DOIs	https://doi.org/10.1109/BIBMW.2010.5703831
State	Published - 2010
Event	2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 - HongKong, China Duration: Dec 18 2010 → Dec 21 2010

Publication series

Name	2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010

Other

Other	2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Country/Territory	China
City	HongKong
Period	12/18/10 → 12/21/10

Keywords

Drug activity prediction
Gene set selection
MoANet
Mode of Action

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

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10.1109/BIBMW.2010.5703831

Cite this

Ma, C., Huang, Y., Chen, H. I., & Chen, Y. (2010). A computational framework for predicting drug Mode-of-Action. In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 (pp. 374-379). Article 5703831 (2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010). https://doi.org/10.1109/BIBMW.2010.5703831

A computational framework for predicting drug Mode-of-Action. / Ma, Chifeng; Huang, Yufei; Chen, Hung I. et al.
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010. 2010. p. 374-379 5703831 (2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010).

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

Ma, C, Huang, Y, Chen, HI & Chen, Y 2010, A computational framework for predicting drug Mode-of-Action. in 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010., 5703831, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010, pp. 374-379, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010, HongKong, China, 12/18/10. https://doi.org/10.1109/BIBMW.2010.5703831

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title = "A computational framework for predicting drug Mode-of-Action",

abstract = "The problem of large scale computational drug screening is considered in this paper. A computational framework is developed for microarray profile of drug treatment from the Connectivity Map (cMap) project. The framework is proposed to address the deficiency of cMap prediction. This scheme includes construction of drug Mode-of-Actions (MoA) and subsequently Mode-of-Action (MoA) network, or MoANet and a MoA prediction algorithm based on MoAs. The scheme is tested on the cMap data and an independent data set. The result shows significant improvement over cMap.",

keywords = "Drug activity prediction, Gene set selection, MoANet, Mode of Action",

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AB - The problem of large scale computational drug screening is considered in this paper. A computational framework is developed for microarray profile of drug treatment from the Connectivity Map (cMap) project. The framework is proposed to address the deficiency of cMap prediction. This scheme includes construction of drug Mode-of-Actions (MoA) and subsequently Mode-of-Action (MoA) network, or MoANet and a MoA prediction algorithm based on MoAs. The scheme is tested on the cMap data and an independent data set. The result shows significant improvement over cMap.

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A computational framework for predicting drug Mode-of-Action

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Keywords

ASJC Scopus subject areas

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