MAY 10, 2017 12:00 PM PDT

Identifying genes "related to" or "specific for" a disease of interest

Speaker
  • CEO, Nebion
    Biography
      Philip Zimmerman pursued a PhD in molecular biology. His post doctoral research focused on genetic regulatory networks and meta-profile analysis. As CEO of Nebion, he is responsible for business development and marketing.

    Abstract

    A plethora of methods and tools have been developed to identify genes differentially expressed between two compared conditions, such as "diseased versus healthy". For many scientists, however, the type of result that has been obtained is not easily interpretable. For example, when comparing a batch of diseased tissue samples to healthy controls, many researchers will interpret the resulting gene list as being "disease-specific". This is actually not the case, since one has only compared samples for one disease and on a single tissue type. The genes obtained are therefore merely "related to a certain degree" to this disease in the tested tissue. To answer the question of "which genes are disease-specific", one needs to include data from many other diseases. This presentation will illustrate this concept using gene expression data from several studies on Crohn's disease and from hundreds of other diseases. The difference between disease-SPECIFIC and disease-RELATED genes will be explained and how such genes can be discovered using a high quality, deeply curated compendium of expression data.


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    MAY 10, 2017 12:00 PM PDT

    Identifying genes "related to" or "specific for" a disease of interest


    Specialty

    T-Cells

    Gene Sequencing

    Drug Discovery

    Personalized Medicine

    Cell Culture

    Immunity

    Biotechnology

    Immunotherapeutics

    Antibodies

    Virology

    Disease

    Immunology

    Dna Sequencing

    Clinical Diagnostics

    Big Data

    Geography

    Asia50%

    Europe50%

    Registration Source

    Website Visitors100%

    Job Title

    Student50%

    Educator/Faculty50%

    Organization

    Academic Institution50%

    Other50%


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