This course is targeted for biomedical scientists looking for working knowledge of programming and statistics. This is a fast-paced, hands-on course covering the following topics: programming basics in Python, probabilities, elements of linear algebra, elements of calculus, and elements of data analytics. Students are expected to learn lecture material outside of the classroom and focus on labs during class. All labs evolve around real-world biomedical and health data sets. Course Director: Karthik Natarajan.
An overview of the field of biomedical informatics, combining perspectives from medicine, computer science, and social science. Use of computers and information in health care and the biomedical sciences, covering specific applications and general methods, current issues, capabilities and limitations of biomedical informatics. Biomedical Informatics studies the organization of medical information, the effective management of information using computer technology, and the impact of such technology on medical research, education, and patient care. The field explores techniques for assessing current information practices, determining the information needs of health care providers and patients, developing interventions using computer technology, and evaluating the impact of those intervention. Course Director: Nicholas Tatonetti.
Survey of foundational symbolic methods for modeling health information systems and for making those models explicit and sharable. The topics cover clinical terminologies (e.g., ICD-9, SNOMED-CT, MeSH, UMLS), biomedical ontologies (e.g., GO, Disease Ontology, PharmGKB), knowledge representation, computerized practice guidelines, semantic interoperability, and text processing. Course Director: Chunhua Weng.
Survey of the computational methods underlying the field of medical informatics. Explores techniques in mathematics, logic, decision science, computer science, engineering, cognitive science, management science and epidemiology, and demonstrates the application to health care and biomedicine. Course Director: Noemie Elhadad.
Provides an overview of research methods relevant to biomedical informatics. The overall goal of the course is to prepare the student to participate in and perform scientific research. Competencies of the course include learning to design a study of a biomedical informatics resource; perform quantitative and qualitative analysis relating to a biomedical informatics resource; and write a biomedical informatics-related research proposal. By the end of the course, all trainees must be able to write a biomedical informatics-related research summary and complete certification in responsible conduct of research. Course Director: Lena Mamykina.
Biological Sequence Analysis introduces the basics of sequential, structural, and functional genomics. The course is both a lecture and lab course, in which students learn the basic bioinformatic principles and apply these principles through laboratory exercises. The course accommodates both students with a computational background with little previous biology, and students from a primarily biological background, with little previous computation. Topics include basic Unix, biological databases, sequence comparison, database searching, multiple sequence alignment, biological regular expressions, profile methods (including hidden Markov models), protein and RNA structure prediction, mapping, primer design, genomic analysis, molecular phylogetics, and functional genomics including microarray analysis and pathway analysis. Course Director: Richard Friedman.
Representative Elective Courses
A practical overview of topics critical to the planning, implementation, and operation of clinical information systems. Includes the governance of and strategic planning and budgeting for information technology efforts, architectural aspects of electronic medical records, health care systems interoperability, systems operations, project management, the legal and regulatory aspects of clinical systems, plus risk assessment and controls. Course Directors: Bruce Forman, Virginia Lorenzi, and Soumitra Sengupta.
This course integrates lectures, labs and a team project in the application of concepts, theories, and skills associated with providing effective solutions to business problems along with the redesign and development of new business processes. This course will use various disciplined business process redesign methodologies to propose redesign solution to a real problem(s) at CUMC. Course Director: Robert Sideli.
Methods in biomedical data science (i.e. translational bioinformatics) for graduate students and upperclassmen. Students study the statistical and computational algorithms to evaluate large biomedical data, including sequence analysis, application of supervised and unsupervised machine learning, graph theoretic models and network analysis, and chemical informatics. They study how to apply these algorithms to biomedical domains in non-human genetics, human genetics, pharmacology, and public health. Successful completion of the course readies the student for graduate level research in translational bioinformatics. Course Director: Nicholas Tatonetti.
Required for C2B2 students in the spring semester. The course will present computational approaches of reconstruction, analysis, and simulation of cellular networks. Metabolic, signaling, and protein-interaction networks will be covered. The networks will be discussed at several levels of structural organization: overall network, functional and structural modules, network motifs. We will emphasize how specific biophysical and biochemical properties of different networks lead to conceptual simplifications for analysis and simulation. Network evolution and similarities between cellular and non-biological networks will be discussed. Course Director: Dennis Vitkup.
Course Director: Raul Rabadan.
Next-generation sequencing (NGS) has become ubiquitous in biomedical research with numerous applications. This course will provide an in-depth introduction to principles of modern sequencing, key computational algorithms and statistical models, and applications in disease genetics, cancer and fundamental biology. It will cover genome, exome and transcriptome sequencing approaches. Emphasis will be placed on understanding the interplay between experimental design, data acquisition, and data analysis so that students can apply these powerful tools in their own research. Course directors: Yufeng Shen & Peter Sims.
This course will be an advanced elective, in the form of a hybrid “graduate seminar” and “project course.” We will examine research on the topic of “clinician information needs,” with particular relation to needs that occur while using clinical information systems. We will also examine methodologies for identifying, and resolving, information needs. Course Director: James Cimino.
Public Health Informatics combines public health research and practice with perspectives from the core underlying components of biomedical informatics e.g., computer science, information science, implementation and organizational science, social, and behavioral science. This course will provide an overview of the needs and uses of information in public health, covering specific applications and general methods, current issues, capabilities and limitations of public health informatics. Course Director: Rita Kukafka.