Genome-Wide Survival Analysis of Somatic Mutations in Cancer

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7821)


Motivation. Next-generation DNA sequencing technologies now enable the measurement of exomes, genomes, and mRNA expression in many samples. The next challenge is to interpret these large quantities of DNA and RNA sequence data. In many human and cancer genomics studies, a major goal is to discover associations between an observed phenotype and a particular variable from genome-wide measurements of many such variables. In this work we consider the problem of testing the association between a DNA sequence variant and the survival time, or length of time that patients live following diagnosis or treatment. This problem is relevant to many cancer sequencing studies, in which one aims to discover somatic variants that distinguish patients with fast-growing tumors that require aggressive treatment from patients with better prognosis [1].


Polynomial Time Approximation Scheme Somatic Variant Fully Polynomial Time Approximation Scheme Permutational Distribution Ovarian Serous Adenocarcinoma 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceBrown UniversityProvidenceUSA
  2. 2.Center for Computational Molecular BiologyBrown UniversityProvidenceUSA

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