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Determining and interpreting correlations in lipidomic networks found in glioblastoma cells

BACKGROUND: Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics...

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Autores principales: Görke, Robert, Meyer-Bäse, Anke, Wagner, Dorothea, He, Huan, Emmett, Mark R, Conrad, Charles A
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2944140/
https://www.ncbi.nlm.nih.gov/pubmed/20819237
http://dx.doi.org/10.1186/1752-0509-4-126
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author Görke, Robert
Meyer-Bäse, Anke
Wagner, Dorothea
He, Huan
Emmett, Mark R
Conrad, Charles A
author_facet Görke, Robert
Meyer-Bäse, Anke
Wagner, Dorothea
He, Huan
Emmett, Mark R
Conrad, Charles A
author_sort Görke, Robert
collection PubMed
description BACKGROUND: Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases. RESULTS: We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma. CONCLUSIONS: The novel computational paradigm provides unique "fingerprints" by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers.
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spelling pubmed-29441402010-10-19 Determining and interpreting correlations in lipidomic networks found in glioblastoma cells Görke, Robert Meyer-Bäse, Anke Wagner, Dorothea He, Huan Emmett, Mark R Conrad, Charles A BMC Syst Biol Research Article BACKGROUND: Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases. RESULTS: We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma. CONCLUSIONS: The novel computational paradigm provides unique "fingerprints" by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers. BioMed Central 2010-09-07 /pmc/articles/PMC2944140/ /pubmed/20819237 http://dx.doi.org/10.1186/1752-0509-4-126 Text en Copyright ©2010 Görke et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Görke, Robert
Meyer-Bäse, Anke
Wagner, Dorothea
He, Huan
Emmett, Mark R
Conrad, Charles A
Determining and interpreting correlations in lipidomic networks found in glioblastoma cells
title Determining and interpreting correlations in lipidomic networks found in glioblastoma cells
title_full Determining and interpreting correlations in lipidomic networks found in glioblastoma cells
title_fullStr Determining and interpreting correlations in lipidomic networks found in glioblastoma cells
title_full_unstemmed Determining and interpreting correlations in lipidomic networks found in glioblastoma cells
title_short Determining and interpreting correlations in lipidomic networks found in glioblastoma cells
title_sort determining and interpreting correlations in lipidomic networks found in glioblastoma cells
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2944140/
https://www.ncbi.nlm.nih.gov/pubmed/20819237
http://dx.doi.org/10.1186/1752-0509-4-126
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