Genome-wide survival analysis of somatic mutations in cancer

Fabio Vandin, Alexandra Papoutsaki, Benjamin J. Raphael, Eli Upfal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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].

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 17th Annual International Conference, RECOMB 2013, Proceedings
Pages285-286
Number of pages2
DOIs
StatePublished - Apr 3 2013
Externally publishedYes
Event17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013 - Beijing, China
Duration: Apr 7 2013Apr 10 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7821 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013
CountryChina
CityBeijing
Period4/7/134/10/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Vandin, F., Papoutsaki, A., Raphael, B. J., & Upfal, E. (2013). Genome-wide survival analysis of somatic mutations in cancer. In Research in Computational Molecular Biology - 17th Annual International Conference, RECOMB 2013, Proceedings (pp. 285-286). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7821 LNBI). https://doi.org/10.1007/978-3-642-37195-0_26