Molecular analysis of gene expression, microbiome, and proteomics data aims to understand biological processes by leveraging high-throughput technologies and data science. Aided by subject matter expertise, this combination has resulted in accelerated discoveries in health and disease. This form of analysis is particularly important for scientists studying how changes in high-throughput molecular measurements can be linked to health and disease mechanisms that could lead to new diagnostic tools and therapeutics. In the context of COVID-19 we seek to understand how the host response quantified via molecular measurements is associated with disease characteristics such as symptoms and severity.
In this session we will go through the characteristics of the molecular data generated by some of these technologies and the fundamental processing and statistical analysis tools (including machine learning methods) that can be used to generate knowledge from these complex, high-dimensional data. The use cases will be framed around the COVID-19 molecular analysis work being done at Duke and other institutions.
This session is part of the Duke+Data Science (+DS) program virtual series on COVID-19 + Data Science.
On the day before the session, all registrants will receive an e-mail with a link and meeting information.