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Gene Set- and Pathway- Centered Knowledge Discovery Assigns Transcriptional Activation Patterns in Brain, Blood, and Colon Cancer: A Bioinformatics Perspective

Gene Set- and Pathway- Centered Knowledge Discovery Assigns Transcriptional Activation Patterns in Brain, Blood, and Colon Cancer: A Bioinformatics Perspective

Lilit Nersisyan, Henry Löffler-Wirth, Arsen Arakelyan, Hans Binder
Copyright: © 2014 |Volume: 4 |Issue: 2 |Pages: 24
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781466655393|DOI: 10.4018/IJKDB.2014070104
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MLA

Nersisyan, Lilit, et al. "Gene Set- and Pathway- Centered Knowledge Discovery Assigns Transcriptional Activation Patterns in Brain, Blood, and Colon Cancer: A Bioinformatics Perspective." IJKDB vol.4, no.2 2014: pp.46-69. http://doi.org/10.4018/IJKDB.2014070104

APA

Nersisyan, L., Löffler-Wirth, H., Arakelyan, A., & Binder, H. (2014). Gene Set- and Pathway- Centered Knowledge Discovery Assigns Transcriptional Activation Patterns in Brain, Blood, and Colon Cancer: A Bioinformatics Perspective. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 4(2), 46-69. http://doi.org/10.4018/IJKDB.2014070104

Chicago

Nersisyan, Lilit, et al. "Gene Set- and Pathway- Centered Knowledge Discovery Assigns Transcriptional Activation Patterns in Brain, Blood, and Colon Cancer: A Bioinformatics Perspective," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 4, no.2: 46-69. http://doi.org/10.4018/IJKDB.2014070104

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Abstract

Genome-wide ‘omics'-assays provide a comprehensive view on the molecular landscapes of healthy and diseased cells. Bioinformatics traditionally pursues a ‘gene-centered' view by extracting lists of genes differentially expressed or methylated between healthy and diseased states. Biological knowledge mining is then performed by applying gene set techniques using libraries of functional gene sets obtained from independent studies. This analysis strategy neglects two facts: (i) that different disease states can be characterized by a series of functional modules of co-regulated genes and (ii) that the topology of the underlying regulatory networks can induce complex expression patterns that require analysis methods beyond traditional genes set techniques. The authors here provide a knowledge discovery method that overcomes these shortcomings. It combines machine learning using self-organizing maps with pathway flow analysis. It extracts and visualizes regulatory modes from molecular omics data, maps them onto selected pathways and estimates the impact of pathway-activity changes. The authors illustrate the performance of the gene set and pathway signal flow methods using expression data of oncogenic pathway activation experiments and of patient data on glioma, B-cell lymphoma and colorectal cancer.

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