Literature DB >> 15920528

Integrative analysis of the cancer transcriptome.

Daniel R Rhodes1, Arul M Chinnaiyan.   

Abstract

DNA microarrays have been widely applied to the study of human cancer, delineating myriad molecular subtypes of cancer, many of which are associated with distinct biological underpinnings, disease progression and treatment response. These primary analyses have begun to decipher the molecular heterogeneity of cancer, but integrative analyses that evaluate cancer transcriptome data in the context of other data sources are often capable of extracting deeper biological insight from the data. Here we discuss several such integrative computational and analytical approaches, including meta-analysis, functional enrichment analysis, interactome analysis, transcriptional network analysis and integrative model system analysis.

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Year:  2005        PMID: 15920528     DOI: 10.1038/ng1570

Source DB:  PubMed          Journal:  Nat Genet        ISSN: 1061-4036            Impact factor:   38.330


  149 in total

1.  Generalized random set framework for functional enrichment analysis using primary genomics datasets.

Authors:  Johannes M Freudenberg; Siva Sivaganesan; Mukta Phatak; Kaustubh Shinde; Mario Medvedovic
Journal:  Bioinformatics       Date:  2010-10-22       Impact factor: 6.937

2.  A computational framework for the topological analysis and targeted disruption of signal transduction networks.

Authors:  Madhukar S Dasika; Anthony Burgard; Costas D Maranas
Journal:  Biophys J       Date:  2006-04-14       Impact factor: 4.033

Review 3.  Current concepts in the molecular genetics of pediatric brain tumors: implications for emerging therapies.

Authors:  Mandeep S Tamber; Krishan Bansal; Muh-Lii Liang; Todd G Mainprize; Bodour Salhia; Paul Northcott; Michael Taylor; James T Rutka
Journal:  Childs Nerv Syst       Date:  2006-09-02       Impact factor: 1.475

Review 4.  Selective Raf inhibition in cancer therapy.

Authors:  Vladimir Khazak; Igor Astsaturov; Ilya G Serebriiskii; Erica A Golemis
Journal:  Expert Opin Ther Targets       Date:  2007-12       Impact factor: 6.902

5.  Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform.

Authors:  Reza Salehi; Stephen C M Tsoi; Marcos G Colazo; Divakar J Ambrose; Claude Robert; Michael K Dyck
Journal:  J Vis Exp       Date:  2017-01-30       Impact factor: 1.355

Review 6.  Clinical uses of microarrays in cancer research.

Authors:  Carl Virtanen; James Woodgett
Journal:  Methods Mol Med       Date:  2008

Review 7.  Immunohistochemistry in diagnostic surgical pathology: contributions of protein life-cycle, use of evidence-based methods and data normalization on interpretation of immunohistochemical stains.

Authors:  Halliday A Idikio
Journal:  Int J Clin Exp Pathol       Date:  2009-11-25

8.  Quantitative analysis of p53 expression in human normal and cancer tissue microarray with global normalization method.

Authors:  Halliday A Idikio
Journal:  Int J Clin Exp Pathol       Date:  2011-06-15

9.  Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis.

Authors:  Ming Yi; Uma Mudunuri; Anney Che; Robert M Stephens
Journal:  BMC Bioinformatics       Date:  2009-06-29       Impact factor: 3.169

10.  Global associations between copy number and transcript mRNA microarray data: an empirical study.

Authors:  Wenjuan Gu; Hyungwon Choi; Debashis Ghosh
Journal:  Cancer Inform       Date:  2008-02-09
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