DIGREM

an integrated web-based platform for detecting effective multi-drug combinations

Minzhe Zhang, Sangin Lee, Bo Yao, Guanghua Xiao, Lin Xu, Yang Xie

Research output: Contribution to journalArticle

Abstract

MOTIVATION: Synergistic drug combinations are a promising approach to achieve a desirable therapeutic effect in complex diseases through the multi-target mechanism. However, in vivo screening of all possible multi-drug combinations remains cost-prohibitive. An effective and robust computational model to predict drug synergy in silico will greatly facilitate this process. RESULTS: We developed DIGREM (Drug-Induced Genomic Response models for identification of Effective Multi-drug combinations), an online tool kit that can effectively predict drug synergy. DIGREM integrates DIGRE, IUPUI_CCBB, gene set-based and correlation-based models for users to predict synergistic drug combinations with dose-response information and drug-treated gene expression profiles. AVAILABILITY AND IMPLEMENTATION: http://lce.biohpc.swmed.edu/drugcombination. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)1792-1794
Number of pages3
JournalBioinformatics (Oxford, England)
Volume35
Issue number10
DOIs
StatePublished - May 15 2019

Fingerprint

Drug Combinations
Web-based
Drugs
Pharmaceutical Preparations
Bioinformatics
Gene expression
Identification (control systems)
Screening
Synergy
Therapeutic Uses
Genes
Computational Biology
Transcriptome
Availability
Predict
Computer Simulation
Costs and Cost Analysis
Gene Expression Profile
Dose-response
Costs

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

DIGREM : an integrated web-based platform for detecting effective multi-drug combinations. / Zhang, Minzhe; Lee, Sangin; Yao, Bo; Xiao, Guanghua; Xu, Lin; Xie, Yang.

In: Bioinformatics (Oxford, England), Vol. 35, No. 10, 15.05.2019, p. 1792-1794.

Research output: Contribution to journalArticle

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