MAY 12, 2016 10:30 AM PDT

Big data integration - Inferring and using individual patient network models

C.E. Credits: CEU
Speaker
  • 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

    Show Resources
    You May Also Like
    SEP 10, 2020 9:00 AM PDT
    C.E. CREDITS
    SEP 10, 2020 9:00 AM PDT
    Date: September 10, 2020 Time: 9:00am (PDT), 12:00pm (EDT) Osmolality testing is relevant throughout the entire bioprocessing workflow. As customers look to refine mAb and gene therapy workf...
    OCT 29, 2020 6:00 AM PDT
    C.E. CREDITS
    OCT 29, 2020 6:00 AM PDT
    Date: October 29, 2020 Time: 6:00am (PDT), 9:00am (EDT), Chronic inflammation can occur as a result of a combination of genetic predispositions and environmental factors. Epigenetic modifica...
    NOV 16, 2020 8:00 AM PST
    C.E. CREDITS
    NOV 16, 2020 8:00 AM PST
    Date: November 16, 2020 Time: 8:00am (PST), 11:00am (EST) CRISPR screening has become the prime discovery tool in modern biomedical research and drug discovery. At the same time, most screen...
    NOV 18, 2020 8:00 AM PST
    C.E. CREDITS
    NOV 18, 2020 8:00 AM PST
    DATE: November 18, 2020 TIME: 08:00am PDT We develop and implement technologies to solve some of the major bottlenecks in biomedical research. In particular, we establish new imaging approac...
    SEP 02, 2020 7:00 AM PDT
    C.E. CREDITS
    SEP 02, 2020 7:00 AM PDT
    DATE: September 2, 2020 TIME: 03:00pm PDT, 6:00pm EDT Spatial omics is an expanding collection of methods to examine biological molecules in their geographical context. By retaining the prec...
    NOV 10, 2020 7:00 AM PST
    C.E. CREDITS
    NOV 10, 2020 7:00 AM PST
    DATE: November 10, 2020 TIME: 7:00am PDT, 10:00am EDT Automation can provide tremendous benefits such as increased pipetting precision and accuracy, productivity, and throughput. Numerous wo...
    Loading Comments...
    Show Resources