BMC Genomics 2017 01 25;18(Suppl 1):1050. Epub 2017 Jan 25.
Department of Medicine, Division of Genomic Medicine, The George Washington University Medical Center, Washington, 20037, D.C., USA.
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BMC Genomics 2014 24;15 Suppl 1:S6. Epub 2014 Jan 24.
Background: Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment. Read More
BMC Syst Biol 2012 17;6 Suppl 3:S13. Epub 2012 Dec 17.
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
Background: Pathway analysis of large-scale omics data assists us with the examination of the cumulative effects of multiple functionally related genes, which are difficult to detect using the traditional single gene/marker analysis. So far, most of the genomic studies have been conducted in a single domain, e.g. Read More
BMC Genomics 2014 Dec 24;15:1181. Epub 2014 Dec 24.
Instituto de Física, Universidade Federal do Rio Grande do Sul, Av, Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil.
Background: Transcriptogram profiling is a method to present and analyze transcription data in a genome-wide scale that reduces noise and facilitates biological interpretation. An ordered gene list is produced, such that the probability that the genes are functionally associated exponentially decays with their distance on the list. This list presents a biological logic, evinced by the selective enrichment of successive intervals with Gene Ontology terms or KEGG pathways. Read More
BJU Int 2011 Jul 16;108(2 Pt 2):E29-35. Epub 2011 Mar 16.
Department of Urology, University of Rostock, Rostock, Germany.
Objective: To improve the workflow for standardizing the statistical interpretation provides an opportunity for the analysis of gene expression in clear cell renal cell carcinoma (ccRCC). RCC as a solid tumour entity represents a very suitable tumour model for such investigations. Although it is possible to investigate expression profiles by microarray technologies, the main problem is how to adequately interpret the accumulated mass of data derived from microarray technologies. Read More