Literature DB >> 17406508

The mode-of-action by network identification (MNI) algorithm: a network biology approach for molecular target identification.

Heming Xing1, Timothy S Gardner.   

Abstract

This protocol details the use of the mode-of-action by network identification (MNI) algorithm to identify the gene targets of a drug treatment based on gene-expression data. Investigators might also use the MNI algorithm to identify the gene mediators of a disease or the physiological state of cells and tissues. The MNI algorithm uses a training data set of hundreds of expression profiles to construct a statistical model of gene-regulatory networks in a cell or tissue. The model describes combinatorial influences of genes on one another. The algorithm then uses the model to filter the expression profile of a particular experimental treatment and thereby distinguish the molecular targets or mediators of the treatment response from hundreds of additional genes that also exhibit expression changes. It takes approximately 1 h per run, although run time is significantly affected by the size of the genome and data set.

Mesh:

Year:  2006        PMID: 17406508     DOI: 10.1038/nprot.2006.300

Source DB:  PubMed          Journal:  Nat Protoc        ISSN: 1750-2799            Impact factor:   13.491


  12 in total

1.  Silencing HoxA1 by intraductal injection of siRNA lipidoid nanoparticles prevents mammary tumor progression in mice.

Authors:  Amy Brock; Silva Krause; Hu Li; Marek Kowalski; Michael S Goldberg; James J Collins; Donald E Ingber
Journal:  Sci Transl Med       Date:  2014-01-01       Impact factor: 17.956

Review 2.  Network analyses in systems pharmacology.

Authors:  Seth I Berger; Ravi Iyengar
Journal:  Bioinformatics       Date:  2009-07-30       Impact factor: 6.937

3.  Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia.

Authors:  Elissa J Cosgrove; Yingchun Zhou; Timothy S Gardner; Eric D Kolaczyk
Journal:  Bioinformatics       Date:  2008-09-08       Impact factor: 6.937

Review 4.  Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.

Authors:  Peter Csermely; Tamás Korcsmáros; Huba J M Kiss; Gábor London; Ruth Nussinov
Journal:  Pharmacol Ther       Date:  2013-02-04       Impact factor: 12.310

5.  Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data.

Authors:  Alexander Mitsos; Ioannis N Melas; Paraskeuas Siminelakis; Aikaterini D Chairakaki; Julio Saez-Rodriguez; Leonidas G Alexopoulos
Journal:  PLoS Comput Biol       Date:  2009-12-04       Impact factor: 4.475

6.  Hierarchical parameter estimation of GRN based on topological analysis.

Authors:  Wei Zhang; Feng Zhang; Jianming Zhang; Ning Wang
Journal:  IET Syst Biol       Date:  2018-12       Impact factor: 1.615

Review 7.  Network based elucidation of drug response: from modulators to targets.

Authors:  Francesco Iorio; Julio Saez-Rodriguez; Diego di Bernardo
Journal:  BMC Syst Biol       Date:  2013-12-13

8.  Inferring gene targets of drugs and chemical compounds from gene expression profiles.

Authors:  Heeju Noh; Rudiyanto Gunawan
Journal:  Bioinformatics       Date:  2016-03-18       Impact factor: 6.937

9.  Combination of a proteomics approach and reengineering of meso scale network models for prediction of mode-of-action for tyrosine kinase inhibitors.

Authors:  Stefan Balabanov; Thomas Wilhelm; Simone Venz; Gunhild Keller; Christian Scharf; Heike Pospisil; Melanie Braig; Christine Barett; Carsten Bokemeyer; Reinhard Walther; Tim H Brümmendorf; Andreas Schuppert
Journal:  PLoS One       Date:  2013-01-09       Impact factor: 3.240

10.  Induction of olfaction and cancer-related genes in mice fed a high-fat diet as assessed through the mode-of-action by network identification analysis.

Authors:  Youngshim Choi; Cheol-Goo Hur; Taesun Park
Journal:  PLoS One       Date:  2013-03-26       Impact factor: 3.240

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