Masters of Science Degree Program in Translational Research


MTR Bioinformatics / Biomedical Informatics

The MTR Bioinformatics/Biomedical Informatics concentration is designed for students who are adopting informatics methodologies to develop and test their own hypotheses. This rapidly expanding field 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 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.

Bioinformatics / Biomedical Informatics Concentration Director: Ben Voight, PhD

Curriculum

Summer Year 1

Fall Year 1

Spring Year 1

MTR 602: Proposal Development

MTR 601: Review Writing

MTR 600: Introductory Biostatistics

MTR 603: Disease Measurement

MTR 604: Scientific and Ethical Conduct

MTR 535: Bioinformatics or other Elective

Summer Year 2

Fall Year 2

Spring Year 2

MTR 605: Data Manuscript Writing

MTR 999: Lab

BMIN 501:  Data Science for Biomedical Informatics or other Elective

MTR 999: Lab

MTR 607:  Thesis Credit

MTR 608:  Thesis Credit

Required Bioinformatics/Biomedical Informatics Course:

MTR 535 Introduction to Bioinformatics
or
BMIN 503 Data Science for Biomedical Informatics

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.

BMIN 503 – 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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