The Master of Biomedical Informatics Program will be offering two new half courses in Precision Medicine led by Isaac Kohane and Paul Avillach.

Please see the course descriptions below, and email Rebecca Fitzhugh your CV and a brief statement of interest if you are interested in enrolling.

BMI 703 - Precision Medicine I: Genomic Medicine 

Course Director: Isaac Kohane

It has been often repeated that the human genome project will transform medicine. In this course we will explore the ways in which genomics is indeed changing the processes of diagnoses and therapy but also the ways in which genomics recasts well-worn medical challenges in the context of the “big data” now generated across multiple measurement modalities in medicine.  Like other scientific revolutions in measurement, but in its own unique way, genomics poses some practical and philosophical challenges about the nature of identity, disease and ethnicity. In the context of the economics of healthcare and the advent of precision medicine, these challenges require careful analytic and data-driven approaches which will be addressed in this course.

BMI 705 - Precision Medicine II: Integrating Clinical and Genomic Data

Course Director: Paul Avillach

The real value in biomedical research lies not in the scale of any single source of data, but in the ability to integrate and interrogate multiple, complementary datasets simultaneously. This course will dive into methods about combining clinical and genomics data across different scales and resolutions to enable new perspectives for essential biomedical questions. It will focus on the development of novel statistical methods and techniques for the integration of multiple heterogeneous clinical and epidemiological cohorts, including Electronic Health Records (EHRs). At the HMS Department of Biomedical Informatics we have built open source technologies to integrate in these cohorts vast and multiple types of high-throughput phenotypic and genotypic data. The students will use those tools during the problem sets with real patient data to learn the process towards making new biomedical discoveries.