Advancing Biomedical Research Through Computational Analysis of Genomic Data
Recent technological advances have driven huge growth in the availability of large, diverse genomic data sets. Despite this explosive growth, the promised revolutionary impact on medicine is being realized much more slowly. Contributing to this delay are computational hurdles in identifying medically-relevant information among the deluge of genomic data.
In this talk, we will discuss ongoing work that both illustrates and addresses these issues. First, we will describe a pilot project in Down syndrome fetuses that illustrates how computational analysis of genomic data can directly suggest novel treatment options. However, such results are still not as common as we would like, in part because of our limited understanding of how genes and proteins relate to each other and to the underlying biological processes that cause disease. We will then introduce several recent computational results, incorporating techniques from graph theory, text mining, and machine learning, designed to help reduce these barriers to medical progress.