An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data | Zendy

Guangxu Jin | Zendy; Hong Zhao | Zendy; Xiaobo Zhou | Zendy; Stephen T.C. Wong | Zendy

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An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data

Author(s) -

Guangxu Jin,

Hong Zhao,

Xiaobo Zhou,

Stephen T.C. Wong

Publication year - 2011

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btr202

Subject(s) - computational biology , pairwise comparison , drug , computer science , microarray analysis techniques , gene , biology , pharmacology , gene expression , artificial intelligence , genetics

Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data. However, such methods cannot be used to identify molecular signaling mechanisms of synergistic drug combinations. In this article, we propose an enhanced Petri-Net (EPN) model to recognize the synergistic effects of drug combinations from the molecular response profiles, i.e. drug-treated microarray data.

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