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Curriculum

MS Degree Students

Students in this program are required to take a minimum of 30 formal course credit hours and complete a master’s thesis.  MS degree students are required to take our five core courses, two selectives (or must have taken the equivalent prior to entrance in the program), two courses in each of two competency areas, and take one additional elective.

PhD Degree Students

The PhD degree requires students to complete a MS degree prior to entering the PhD program.  Students in this program are also required to take a minimum of 72 credits (including research credits).  In addition to earning the MS degree, PhD students must complete a teaching practicum, pass a qualifying examination, and successfully propose and defend a dissertation.  PhD students take our five core courses, three selectives, two graduate courses in each competency areas, and three additional electives.

Areas of Concentration

Our curriculum is adapted to students’ backgrounds and concentration area.  Students in the MS and PhD degree programs typically develop an area of concentration that is supported by their elective coursework and research experiences to address a set of challenging questions in our field.  Examples of these concentration areas include:

The Clinical Informatics training area primarily focuses on the design, implementation, and evaluation of information technologies in the clinical domain. The application of informatics to direct patient care, such as advanced decision support and person-centered health records – is exceptionally well represented in the research of DBMI faculty, providing opportunities to develop research with state-of-the-art applications and real-world settings. This unique learning environment includes readily accessible, large-scale operational clinical information systems in which new scientific hypotheses can be tested and new technologies can be deployed.

The Translational Bioinformatics training area focuses on the relationship between molecular (e.g., genomic and proteomic) data and medical outcomes (e.g., clinical phenotypes). The application of informatics to support basic research in such areas as genomics, proteomics, and systems biology is at Vanderbilt closely coupled to the institutional vision for Precision Medicine, in which high throughput molecular measurements (DNA, RNA, proteins) not only are applied for scientific discovery, but also for rapid translation to practice.

The Data Science track welcomed its first students in Fall 2016. It should be recognized that Data Science, and big data science in particular, is certainly related to the existing training areas, particularly as health information technology evolves from standalone solitary systems to learning health systems. Yet, there is a clear distinction between Data Science and the existing training areas in that data science focuses more on how to manage the associated data in a cost effective and scalable manner, analyze the data in a statistically rigorous fashion, and apply the data in modern computing infrastructure that is tailored to the semantics of biomedical data.

New for 2019, the Environmental Exposures Emphasis (E3) track prepares students to pursue independent research careers spanning the fields of bioinformatics and environmental health sciences. This new collaborative training pathway, in partnership with the Training Program in Environmental Toxicology, provides specialized training in environmental exposure via coursework in toxicology, BCHM 8336: Biochemical and Molecular Toxicology. E3 trainees will specialize in allied areas through elective coursework, research rotations, seminars, and workshops; integrate into the laboratories of Environmental Toxicology Program faculty to provide dissertation research projects within the NIEHS training mission; and have co-mentors in the BMI Training Program.

Core Courses

Unless waived because of satisfactory completion of a prior similar course, all candidates for the MS and PhD degrees are required to take the following five core courses in Biomedical Informatics:

  • BMIF 6300. Foundations of Biomedical Informatics and Evidence-Based Practice
  • BMIF 6310. Foundations of Bioinformatics and Computational Biology
  • BMIF 6315. Methodological Foundations of Biomedical Informatics
  • BMIF 6321-6322. Scientific Communication
  • BMIF 6341-6342. Research Rotation in Biomedical Informatics

Selectives

M.S. students must choose at least two selectives from this list and PhD students must choose at least three selectives from this list.

  • BMIF 7311. Systems Biology
  • BMIF 7320. Healthcare System and Informatics
  • BMIF 7330. Machine Learning for Biomedicine
  • BMIF 7340. Clinical Information Systems and Databases
  • BMIF 7360. Clinical Research Informatics
  • BMIF 7370. Evaluation Methods in Biomedical Informatics
  • BMIF 7380. Data Privacy in Biomedicine
  • BMIF 7391. Technology and Society (Fall)
  • BMIF 7391. Data Management for Clinical & Translational Research (Spring)

Additional courses in development include:

  • Informatics of Systems Biology
  • Biological Networks

Competency Areas

Degree candidates are expected to demonstrate advanced knowledge of identified topics in three competency areas: Computer Science/ Informatics, Biological and Health Sciences, and Research Methods. MS degree students take two courses in each of two competency areas and PhD students take two courses in each of three competency areas. Some or all of these courses may have been taken prior to beginning this program – see below for details.

The Competency Areas are as follows:

  • Computer Science/Informatics: compilers and formal languages, complexity, computability, computer organization, databases, data structures, design and analysis of algorithms, networks, operating systems, programming languages, software engineering.
  • Biological and Health Sciences: principles of cell, organism, and population biology; anatomy, physiology, and mechanisms of disease; nosology, diagnostics, and therapeutics; genetics and molecular medicine.
  • Research Methods: mathematics for computer science (discrete mathematics, probability theory); mathematical statistics, applied statistics, biostatistics, and mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability); qualitative and quantitative research designs, epidemiology, and methods of systems evaluation.

Electives

Students in the MS degree program are required to take one elective course relevant to their educational goals and research focus. Students in the PhD degree program are required to take three elective courses. Options for elective courses include courses in Biomedical Informatics that would not otherwise be required of the student, independent study with faculty members, and courses from the competency fields. Other elective courses may be selected if approved by the faculty mentor/research adviser and the Director of Graduate Studies.

DBMI Seminar Series

All students must attend and participate in the DBMI Seminar Series during their course of study. Participation is logged and students are expected to attend 75% of offered seminars. CME for clinicians is available. All students must also participate in the journal club and the ethical conduct of research.