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Biomedical Informatics Courses

Our curriculum leverages the large number of relevant courses taught on campus and supplements them with BMI courses that provide specialized domain knowledge and medical context. We emphasize courses that are project-oriented, where students learn to formulate, implement, and evaluate models within the healthcare environment. All courses at UCSD operate on a quarterly basis. All MED courses below are taught by DBMI faculty and research staff. Typical quarter in which courses as offered is coded as W(Winter), S(Spring), and F(Fall).

Required for all trainees:

  • MED 262: Current Trends in Biomedical Informatics (F, W, S, 1 credit, 6 credits required): Weekly talks by researchers introduce students to current research topics within BMI. Speakers are drawn from academia, health care organizations, industry, and government. As a curriculum requirement, DBMI Ph.D. students are required to complete 6 quarters (total) of MED 262.
  • MED 264: Principles of Biomedical Informatics (F, 4 credits): Students are introduced to the fundamental principles of BMI and to the problems that define modern healthcare. The extent to which BMI can address healthcare problems is explored. Topics covered include structuring of data, computing with phenotypes, integration of molecular, image and other non-traditional data types into electronic medical records, clinical decision support systems, biomedical ontologies, data and communication standards, data aggregation, and knowledge discovery.
  • MED 265: Informatics in Clinical Environments (S, offered alternate years, 4 credits): Students are introduced to the basics of healthcare systems and clinical information needs through reading, online lectures/exercises and class discussion. Students are introduced to medical language, disease processes, and health care practices to provide context within primary, specialty, emergency, and inpatient sites in conjunction with clinical faculty affiliated with the training program. Students examine how clinicians use history-taking, physical examination and diagnostic testing to establish diagnoses and prognoses. Medical decision-making is introduced in the context of available informatics tools and clinical documentation and communication processes. We will also explore important topics relevant to informatics, such as population health, equity, and how patients and providers experience care delivery. The course encourages students to think critically of the processes they observe and formulate hypotheses about how informatics solutions can modify the processes.
  • MED 267: Modeling Clinical Data and Knowledge for Computation (W, 4 credits): This course describes existing methods for representing and communicating biomedical knowledge. The course describes existing health care standards and modeling principles required for implementing data standards, including biomedical ontologies, standardized terminologies, and knowledge resources.

Other courses offered:

  • MED 263: Bioinformatics Applications to Human Disease (W, 4 credits): Students learn background knowledge and practical skills for investigating the biological basis for human disease. Using bioinformatics applications, they: (1) query biological and genetic sequence databases relevant to human health, (2) manipulate sequence data for alignment, recombination, selection, and phylogenetic analysis, (3) normalize microarray data and identify differentially expressed genes and biomarkers between patient groups, (4) annotate protein data and visualize protein structure, and (5) search the human genome and annotate genes relevant to human diseases.
  • MED 268: Statistics Concepts for Biomedical Research (F 2024, 4 credits): This course introduces statistics methods for basic, pre-clinical, and clinical research. Topics include descriptive statistics, t-tests, ANOVA, linear and logistic regression, survival analysis, power and sample size, non-parametric methods, and factorial experiment design. Emphasis is on applications rather than theorems and proofs. Students will gain the ability to design efficient and informative basic research and clinical trials, to perform statistical analyses using the R statistics software, and to critique statistical results in published biomedical research
  • MED 276: Grant Proposal Writing Practicum (S,  offered alternate years, 2 credits):  The focus of this course will be on grant writing and developing persuasive arguments. Previously submitted funded and non-funded grants will be used to illustrate revision and response to reviewers, as well as to provide source materials to perform mock study section reviews. This course will help students write their first grant proposal and understand the process of proposal scoring and reviewing.
  • MED 277: Introduction to Biomedical Natural Language Processing (F 4 credits): Biomedical Natural Language Processing (BioNLP) is an essential tool in both biomedical research and clinical applications. Students taking this course will learn how to process free text data and their integration with other types of biomedical data with BioNLP.
  • MED 278: Cancer Genomics Journal Club (S, F, W 1 Credit): This course is a weekly journal club focused on cancer genomics. With the advances in sequencing and big data analysis, the field of cancer genomics has become extremely complex and sometimes not approachable by biology or medical students. On the other hand engineers, mathematicians or computational biologist sometimes struggle to identify concrete application of their work to the medical and healthcare field. This journal club will offer to both categories of students a venue to get exposure to some of the most advances progress in the field for cancer genomics and precision medicine.  Students will learn the state of the art methods and latest findings in cancer genomics, and learn how to critically review such results and place them in the context of other studies and findings.
  • MED 299 Cloud Computing (S 2021, 4 credits):  This course introduces the core concepts of secure and privacy-preserving computing on the Cloud with an emphasis on Behavioral, Social, and Health Data. Whether you are an informatics student, looking into scaling your applications in a HIPAA compliant cloud environment, or somebody who likes to learn more about informatics-related resources available on the Cloud, this course is for you. 
  • FMPH 431 Special Topics in Public Health: Public Health Informatics (S 2021, 4 credits):  This course will introduce the students to the discipline of public health informatics.  Students will learn informatics concepts, techniques and systems that support public health practice. The course explores public health information systems including immunization information systems (IIS), disease surveillance systems (i.e., BioSense), disease reporting (ELR), registries (i.e., cancer registries), outbreak management (case investigation/contact tracing), and vital statistics.  Students are exposed to prevailing data standards (PHIN, HL7, IHE), data representation (biomedical terminologies), and interoperability with electronic health record systems (EHRs).  The course introduces the student to basic data management skills and concepts through demonstrations and hands-on lab exercises.
  • Biomedical Informatics Journal Club (informational only and not a course):  DBMI has a Biomedical Informatics (BMI) Journal Club that meets every Thursday (from 10:30 a.m. to 11:30 a.m.) to present and critique literature on machine learning, signal processing, and computation in biomedicine. The Journal Club meetings are moderated by Dr. Shamim Nemati.  Anyone interested can email organizers at and will be added to the Zoom meeting calendar invite.
  • MED 299 Intro to Data Science (Summer 2024):  This course will be project-based and cover a range of topic areas and vary according to the intern's background and interests. Past projects have included predictive modeling for personalized medicine; privacy technology; natural language processing; image processing and retrieval; integration of genotypic, phenotypic, and behavioral patient data for discovery; informed consent ontology/tools; and blockchain technology.