MAY 12, 2016 10:30 AM PDT
Big data integration - Inferring and using individual patient network models
Presented at the Genetics and Genomics Virtual Event
CONTINUING EDUCATION (CME/CE/CEU) CREDITS: CEU | P.A.C.E. CE
5 16 174

Speakers:
  • Research Fellow, Biostatistics & Computational Biology, Dana-Farber Cancer Institute
    Biography
      Marieke Kuijjer is a postdoctoral fellow in the laboratory of John Quackenbush at the department of Biostatistics and Computational Biology of the Dana-Farber Cancer Institute and the department of Biostatistics of the Harvard T.H. Chan School of Public Health, Boston, MA. She obtained her PhD (2013) in Cancer Genomics from Leiden University Medical Center, Leiden, the Netherlands, working in the department of Pathology on bioinformatics of bone and soft tissue sarcomas. Marieke's research interests focus around developing and using new methods to map the complex patterns that are disrupted in cancer in networks. These range from integrating gene regulation by transcription factors and microRNAs to discover new cancer subtypes and analyzing single-sample networks in the context of cancer prognosis to using networks to model somatic mutations in cancer.

    Abstract:

    The biological state of the cell is characterized by a complex network of interacting genes, gene products, proteins, microRNAs, as well as other molecules. Microarrays and next generation sequencing technologies have been widely applied to study alterations of these molecules in complex diseases such as cancer. However, most of the standard, widely-used methods for bioinformatics analysis treat these various sources of information independently, looking for overlaps in feature sets rather than directly modeling their interactions.
    It has become clear that, to understand what drives (complex) disease, we need to integrate multiple types of ‘omics data in a natural way that allows us to gain insight into the molecular interactions that occur in disease development and progression. Gene regulatory network reconstruction algorithms infer such interactions by drawing on large numbers of measured expression samples to estimate an “aggregate” network model, which represents single estimates for the likelihood of molecular interactions. While informative, aggregate models fail to capture the heterogeneity represented in a disease population. In this presentation, I will introduce a computational framework for single-sample network reconstruction that allows us to “extract” individual patient networks from aggregate networks. I will demonstrate the strengths of this method in multiple big 'omics datasets, and will highlight newly identified gene regulatory interactions that play a role in cancer survival.

    Learning objectives:

    • Participants will learn how to integrate big 'omics data using gene regulatory networks
    • Participants will learn how to reconstruct and analyze patient-specific gene regulatory networks

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