Courses

Class Descriptions

6300. Foundations of Biomedical Informatics.

This introductory course examines the unique characteristics of clinical and life science data and the methods for representation and transformation of health data, information, and knowledge to improve health care. Principles of information security and confidentiality are taught, along with functional components of information systems in clinical settings and the use of databases for outcome management. Through skill modules, the course provides an introduction to methods underlying many biomedical informatics applications, including information retrieval, medical decision making, evaluation of evidence and knowledge representation. The historical evaluation of the field of biomedical informatics is taught concurrently, using examples of landmark systems developed by pioneers in the field. FALL [3] Frisse

6310. Foundations of Bioinformatics.

This survey course introduces students to the experimental context and implementation of key algorithms in bioinformatics. The class begins with a review of basic biochemistry and molecular biology. Algorithms for matching, aligning, and comparing biological sequences will be evaluated in the context of molecular evolution. The class will examine the systems developed to enable high-throughput DNA sequencing and genome sequence analysis. The informatics associated with microarrays for the quantitative analysis of transcription will follow. Next, the course will consider the algorithms supporting proteomic mass spectrometry and protein structure inference and prediction. Finally, the class will examine the tools of systems biology, including genetic regulatory networks, gene ontologies, and data integration. Formal training in software development is helpful but not required. Students will write and present individual projects. Undergraduates need the permission of the instructor to enroll. FALL [3] Lopez

6311. Systems Biology.

This survey course presents the student with the historical, conceptual and technical foundations of systems biology as it relates to biomedical research using model systems as well as human disease. Prerequisite: BMIF 310 Foundations of Bioinformatics. SPRING [3] 

6315. Methodological Foundations of Biomedical Informatics.

In this course, students will develop foundational concepts of computation and analytical thinking that are instrumental in solving challenging problems in biomedical informatics. The course will use lectures and projects directed by co-instructors and guest lecturers. SPR [3] D Giuse, 

6321. Scientific Communication.

The course will enhance students’ skills in written and oral scientific communication. An introductory segment covers categories of scientific writing, the peer review process, and ethical issues in research communication. Through a two-semester sequence, it provides direct, hands-on experience in writing papers, abstracts, and grant proposals; critiquing and copy editing; and, preparing and giving presentations for scientific meetings. FALL [1] R Miller, T Rosenbloom

6322. Scientific Communication.

See 316a. SPR [1] R Miller, T Rosenbloom

6341. Research Rotation in Biomedical Informatics.

Students will perform research under the direction of a faculty advisor. FALL [1]

6342. Research Rotation in Biomedical Informatics.

See 318a. SPR [1]

7320. Healthcare Systems and Informatics.

The purpose of the Healthcare Systems and Informatics course is for students to understand the organizational world in which they will spend most of their professional lives. A better understanding will lead to strategies to build partnerships with physicians, researchers, hospitals and academic organizations. In turn, better understanding will lead to working more closely as a team in planning future directions and implementing technological programs and changes. This course provides an overview of theoretical concepts as well as the practical tools for the student to understand and work effectively with three main topic areas: (1) understanding health care organizations, especially academic health centers; (2) understanding the health care environment; and (3) understanding organizational informatics, including leadership and people issues. Prerequisite: BMIF 300. SPR [3] N Lorenzi

7330. Machine Learning for Biomedicine.

This course builds on the material covered in Methodological Foundations of Biomedical Informatics (BMIF 315) by introducing several additional machine learning concepts and algorithms with a focus on biomedical decision making and discovery. Even though biomedical applications and examples will be discussed, the methods have broad applicability in science and engineering. The following topics will be covered in this course (may be expanded or modified based on the background of the class participants): decision support systems, cognitive issues in decision making, Bayesian networks, data pre-processing for machine learning, decision trees, clustering, K-Nearest neighbors, neural networks, SVM regression and unsupervised SVMs, Bayesian network learning, and causal discovery using Bayesian networks. Prerequisites: for Biomedical Informatics students: BMIF315; for non-Biomedical Informatics students: a course in data structures or algorithm design and analysis, the ability to program in Matlab version 6 or later, and basic concepts of machine learning and fundamental mathematical concepts needed in machine learning at the level covered in BMIF315. SPR [3] Staff

7340. Clinical Information Systems and Databases.

This course builds on material covered in Methodological Foundations of Biomedical Informatics (BMIF 315) by introducing and developing concepts in distributed systems and network computing: OSI stack, protocols, TCP/IP, Sockets, and DNS; clinical database concepts: synchronization, concurrency, deadlock, full-text databases; distributed database services, including high-availability techniques; and architectural considerations in the design of clinical information systems. The VUMC clinical database architecture is used as a case study. Prerequisites: for Biomedical Informatics students: BMIF315 or permission of instructor; for non-Biomedical Informatics students: coding ability in some standard procedural or object-oriented computer language, preferably PERL. FALL [3] D Giuse

7360. Graduate Seminar on Biomedical Informatics Algorithms.

Graduate-level topics in intermediate or advanced algorithms, data structures, and knowledge representations for biomedical informatics that are not covered in the MS/PhD core courses. Note: covered topics will be highly dependent on faculty and student interests and will change from year to year to reflect research advances and interests. Students must obtain instructor permission to enter the class. [1-3] (Not currently offered.)

7369. Master’s Thesis Research.

7370. Evaluation Methods in Biomedical Informatics.

Students are introduced to evaluation and experimentation, with exposure to study design, including sampling, appropriate use of controls; data collection, including human subjects research considerations; analysis, including testing for statistical significance, definitions of sensitivity and specificity, ROC plots; and reporting of results. Quantitative and qualitative methods will be covered, as well as methods and issues specific to healthcare settings. FALL [3] C Gadd, J Peterson

7379. Non-Candidate Research.

7380. Data Privacy in Biomedicine.

This course introduces students to concepts for evaluating and constructing technologies that protect personal privacy in data collected for primary care and biomedical research. Material in this course touches on topics in biomedical knowledge modeling, data mining, policy design, and law. Prerequisite: Students are expected to be proficient in writing basic software programs; although no specific programming language is required. SPR [3] B Malin

7395. Directed Research / Independent Study.

Students will work under close supervision of a specific faculty member on an ongoing research problem. Depending on the specific project, students will learn aspects of study design, research methods, data collection and analysis, research manuscript writing, and human factors engineering. SPRING/FALL [1-3] Staff.

7399. Ph.D. Dissertation Research.