Jie Yang : Biomedical Big Data and Permanental Classification Approach

Posted by Jie Yang , part of the Statistics and Data Science Seminar.

At

Jan. 20, 2016, 4 p.m.

In

SEO 636

Abstract

The explosion in the availability of biomedical data is creating both great opportunities and challenges for collaborative research among clinicians, genomics and proteomics scientists, molecular biologists, and statisticians. On one hand, electronic medical records and genomic data of a large cohort of individuals are assembled and become available for health study researches. On the other hand, the combined data are extremely high-dimensional and also becoming bigger and bigger, especially the genomic part. As one of the most critical application areas with the biomedical big data, precision medicine refers to precisely classifying individuals into subpopulations according to their susceptibility to a particular disease and precisely tailoring of medical treatments to subcategories of the disease. Achieving the goals of precision medicine requires combining data across multiple formats and developing novel, sophisticated statistical methods. Our permanental classification approach recently developed is capable of handling high-dimensional classification problems. It provides a promising solution for biomedical high-dimensional data, implemented using the most popular open source statistical software, R.