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Systematic Use of Computational Methods Allows Stratifying Treatment Responders in Glioblastoma Multiforme (bibtex)
Systematic Use of Computational Methods Allows Stratifying Treatment Responders in Glioblastoma Multiforme (bibtex)
by Louhimo, Riku, Kaski, Samuel, Rogojin, Vladimir, Lahti, Leo Mikael, Valo, Erkka Antero, Ovaska, Kristian, Chen, Ping, Laakso, Marko, Faisal, Ali, Aittomaki, Viljami and Hautaniemi, Sampsa
Abstract:
Cancers are complex diseases whose comprehensive characterization requires genome-scale molecular data at several levels from genetics to transcriptomics and clinical data. We use our recently published Anduril framework and introduce novel approaches, such as dependency analysis, to identify key variables at miRNA, copy number variation, expression, methylation and pathway level in glioblastoma multiforme (GBM) progression and drug resistance. We also present methods to identify characteristics of clinically relevant subgroups, such as patients treated with temozolomide drug and patients with an EGFRvIII mutation, which is a constitutively active variant of EGFR. Our results identify several novel genomic regions and transcript profiles that may contribute to GBM progression and drug resistance. All results and Anduril scripts are available at http://csbi.ltdk.helsinki.fi/camda/.
Reference:
Systematic Use of Computational Methods Allows Stratifying Treatment Responders in Glioblastoma Multiforme (Louhimo, Riku, Kaski, Samuel, Rogojin, Vladimir, Lahti, Leo Mikael, Valo, Erkka Antero, Ovaska, Kristian, Chen, Ping, Laakso, Marko, Faisal, Ali, Aittomaki, Viljami and Hautaniemi, Sampsa), In Critical Assessment of Massive Data Analysis (CAMDA) workshop, 2011.
Bibtex Entry:
@InProceedings{inp709,
  author    = {Louhimo, Riku AND Kaski, Samuel AND Rogojin, Vladimir AND Lahti, Leo Mikael AND Valo, Erkka Antero AND Ovaska, Kristian AND Chen, Ping AND Laakso, Marko AND Faisal, Ali AND Aittomaki, Viljami AND Hautaniemi, Sampsa},
  title     = {Systematic Use of Computational Methods Allows Stratifying Treatment Responders in Glioblastoma Multiforme},
  booktitle = {Critical Assessment of Massive Data Analysis (CAMDA) workshop},
  year      = {2011},
  abstract  = {Cancers are complex diseases whose comprehensive characterization requires genome-scale molecular data at several levels from genetics to transcriptomics and clinical data. We use our recently published Anduril framework and introduce novel approaches, such as dependency analysis, to identify key variables at miRNA, copy number variation, expression, methylation and pathway level in glioblastoma multiforme (GBM) progression and drug resistance. We also present methods to identify characteristics of clinically relevant subgroups, such as patients treated with temozolomide drug and patients with an EGFRvIII mutation, which is a constitutively active variant of EGFR. Our results identify several novel genomic regions and transcript profiles that may contribute to GBM progression and drug resistance. All results and Anduril scripts are available at http://csbi.ltdk.helsinki.fi/camda/.},
}
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