Masters of Science Degree Program in Translational Research

MTR Biomedical Informatics

Students may complete the traditional MTR Curriculum or elect an optional specialized track. Learn more about the traditional MTR Curriculum here.

The MTR Biomedical Informatics track aims to train a new generation of translational scientists in informatics approaches. The rapidly expanding field of biomedical informatics defines how we compare and evaluate healthcare data to both understand and introduce improvements to care (biomedical informatics), as well as the use of healthcare data to conduct discovery-based investigation of biological systems (bioinformatics). Our goal in introducing the biomedical informatics track within the MTR program is to not only produce translational scientists who are customers and collaborators with informaticians, but to empower these scientists to leverage informatics approaches to develop and test their own hypotheses.


MTR Biomedical Informatics Track

MTR Core and Track Specific Courses

MTR Core Courses:

  • MTR 600 Introductory Biostatistics
  • MTR 601 Review Writing
  • MTR 602 Proposal Development
  • MTR 603 Disease Measurement
  • MTR 604 Scientific and Ethical Conduct
  • MTR 605 Manuscript Writing
  • MTR 999 Lab

Track Specific Courses:

  • EPID 632 Introduction to Biomedical and Health Informatics
  • EPID 600 Data Science for Biomedical Informatics or MTR 535 Introduction to Bioinformatics
  • MTR 999 Lab with Biomedical Informatics Focus

Thesis with an Emphasis on Biomedical Informatics:

  • MTR 607 Thesis
  • MTR 608 Thesis

Track specific course descriptions:

EPID 632 – Introduction to Biomedical and Health Informatics. Course director: John H. Holmes, PhD. This course is offered during the fall semester and is designed to provide a survey of the major topics areas in medical informatics, especially as they apply to clinical research.  Through a series of lectures and demonstrations, students will learn about topics such as databases, natural language, clinical information systems, networks, artificial intelligence and machine learning applications, decision support, imaging and graphics, and the use of computers in education.

MTR 535 – Introduction to Bioinformatics. Course directors: Benjamin F. Voight, PhD and Casey S. Greene, PhD. This course provides broad overview of bioinformatics and computational biology as applied to biomedical research. A primary objective of the course is to enable students to integrate modern bioinformatics tools into their research activities. Course material is aimed to address biological questions using computational approaches and the analysis of data. Areas include DNA sequence alignment, genetic variation and analysis, motif discovery, study design for high-throughput sequencing, RNA and gene expression, single gene and whole-genome analysis, machine learning, and topics in systems biology. The relevant principles underlying methods used for analysis in these areas will be introduced and discussed at a level appropriate for biologists without a background in computer science. However, a basic primer in programming and operating in a UNIX environment will be presented, and students will also be introduced to Python, R, and tools for reproducible research. This course emphasizes direct, hands-on experience with applications to current biological research problems.

EPID 600 – Data Science for Biomedical Informatics. Course director: Blanca Himes, PhD. This course is offered in the fall semester, and we will use R and other freely available software to learn fundamental data science applied to a range of biomedical informatics topics, including those making use of health and genomic data. After completing this course, students will be able to retrieve and clean data, perform exploratory analyses, build models to answer scientific questions, and present visually appealing results to accompany data analyses; be familiar with various biomedical data types and resources related to them; and know how to create reproducible and easily shareable results with R and github. 


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