Description

Understanding intratumor heterogeneity and their driver genes is critical to

designing personalized treatments and improving clinical outcomes of cancers. Such

investigations require accurate delineation of the subclonal composition of a tumor, which

to

Understanding intratumor heterogeneity and their driver genes is critical to

designing personalized treatments and improving clinical outcomes of cancers. Such

investigations require accurate delineation of the subclonal composition of a tumor, which

to date can only be reliably inferred from deep-sequencing data (>300x depth). The

resulting algorithm from the work presented here, incorporates an adaptive error model

into statistical decomposition of mixed populations, which corrects the mean-variance

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Contributors
Date Created
  • 2019
Resource Type
  • Text
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    Note
    • Partial requirement for: Ph.D., Arizona State University, 2019
      Note type
      thesis
    • Includes bibliographical references
      Note type
      bibliography
    • Field of study: Bioinformatics

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    by Navid Ahmadinejad

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