CIBM trainees are expected to gain knowledge in Biomedical Informatics, Biostatistics, Biological Sciences, and Computer Sciences. The CIBM Management Committee periodically reviews each trainee’s course curriculum and expects that approximately five or six courses will be taken to fulfill the core curriculum of CIBM. Courses taken as an undergraduate can be used, pending approval, to satisfy the CIBM course requirements, or substitutions made with the approval of the CIBM Management Committee.
Requirements
Trainees are required to complete required courses, the CIBM Seminar Series, the CIBM Annual Meeting, and a CIBM Annual Review to fulfill the CIBM Training Program curriculum. Students must receive a grade of B or better for a course to count toward the CIBM requirements. Students should also note that the University of Wisconsin-Madison requires that PhD students complete a minor, which typically involves four courses (12 credits) taken outside of ones home department. Courses taken can count for both the PhD minor and for the CIBM requirement.
The introductory course in bioinformatics is required.
- BMI/CS 576 – Introduction to Bioinformatics
One of the following courses is required.
- BMI 573 – Foundations of Data-driven Healthcare
- Med/BMI 918 – Health Informatics for Medical Students
- BMI 773 – Clinical Research Informatics
One of the following two courses is required.
- BMI/STAT 541 – Introduction to Biostatistics
- Stat 571 – Statistical Methods for Bioscience I
There are many options in this category. Specific biomedical sciences courses are selected for each trainee depending on their research focus. Some of the more commonly used classes are listed below
- Gen 466 – General Genetics
- Pop Hlth 471 – Introduction to Environmental Health
- Biochem 501 – Introduction to Biochemistry
- Microbiol 528 – Immunology
- Gen 565 – Human Genetics
- Bioch 601 – Protein and Enzyme Structure and Function
- Bioch 630 – Cellular Signal Transduction Mechanisms
- Gen 626 – Genomic Science
- Pop Hlth 794 – Biological Basis of Population Health
There are many options in this category. Specific computer science courses are selected for each trainee depending on their research focus. Some of the more commonly used classes are listed below
CS 524 – Introduction to Optimization: Introduction to mathematical optimization from a modeling and solution perspective. Formulation of applications as discrete and continuous optimization problems and equilibrium models. Survey and appropriate usage of basic algorithms, data and software tools, including modeling languages and subroutine libraries.
CS 540 – Introduction to Artificial Intelligence: Principles of knowledge-based search techniques; automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.
CS 559 – Computer Graphics: Survey of computer graphics. Image representation, formation, presentation, composition and manipulation. Modeling, transformation, and display of geometric objects in two and three dimensions. Representation of curves and surfaces. Rendering, animation, multi-media and visualization.
CS 564 – Database Management Systems: Design and Implementation: What a database management system is; different data models currently used to structure the logical view of the database: relational, hierarchical, and network. Hands-on experience with relational and network-based database sytems. Implementation techniques for database systems. File organization, query processing, concurrency control, rollback and recovery, integrity and consistency, and view implementation.
CS 577 – Introduction to Algorithms: Survey of important and useful algorithms for sorting, searching, pattern-matching, graph manipulation, geometry, and cryptography. Paradigms for algorithm design, hints for efficient implementation.
CS 570 – Introduction to Human-Computer Interaction: User-centered software design; (1) principles of and methods for understanding user needs, designing and prototyping interface solutions, and evaluating their usability, (2) their applications in designing web-based, mobile, and embodied interfaces through month long group projects.
CS 635 – Tools and Environments for Optimization: Formulation and modeling of applications from computer sciences, operations research, business, science and engineering involving optimization and equilibrium models. Survey and appropriate usage of software tools for solving such problems, including modeling language use, automatic differentiation, subroutine libraries and web-based optimization tools and environments.
CS 642 – Introduction to Information Security: The course covers a wide range of topics on information security, such as, cryptographic primitives, security protocols, system security, and emerging topics.
CS 760 – Machine Learning: Computational approaches to learning including various machine learning paradigms, algorithms, and methodologies for evaluating learning systems. Methods covered include decision trees, instance-based learning, neural networks, support vector machines, ensemble methods, and probabilistic graphical models.
CS 766 – Computer Vision: Fundamentals of image analysis and computer vision; image acquisition and geometry; image enhancement; recovery of physical scene characteristics; shape-from techniques; segmentation and perceptual organization; representation and description of two-dimensional objects; shape analysis; texture analysis; goal-directed and model-based systems; parallel algorithms and special-purpose architectures.
Trainees take an additional elective in biomedical informatics or computer science. Some of the courses in the former category are listed below. Courses in the latter category include those in the list above.
ISyE 517 – Decision Making in Health Care: Introduction to the use of decision sciences in health-care. Conceptual understanding of medical decision making and its tools including decision trees, sensitivity analysis, Markov (decision) processes, and Monte Carlo simulations with examples from the current medical literature.
ISyE/BMI 617 – Health Information Systems: Covers core concepts of health information systems. Major applications include clinical information systems, language and standards, decision support, image technology and digital libraries.p>
BMI 567 – Medical Image Analysis: Presents introductory medical image processing and analysis techniques. Topics include medical imaging formats, segmentation, registration, image quantification, classification.
Biochem/BMI 609 – Mathematical Methods for Systems Biology: Intended to provide a rigorous foundation for mathematical modeling of biological systems. Mathematical techniques include dynamical systems and differential equations. Applications to biological pathways, including understanding of bistability within chemical reaction systems, are emphasized.
BMI 767 – Computational Methods for Medical Image Analysis: Study of computational techniques that facilitate automated analysis, manipulation, denoising, and improvement of large-scale and high resolution medical images. Design and implementation of methods from computer Vision and Machine Learning to efficiently process such image data to answer biologically and clinically meaningful scientific questions.
BMI/CS 776 – Advanced Bioinformatics: Advanced course covering computational problems in molecular biology. The course will study algorithms for problems such as: modeling sequence classes and features, phylogenetic tree construction, gene-expression data protein and RNA structure prediction, and whole-genome analysis and comparisons..
CBE 782 – Modeling Biological Systems: Literature survey of mathematical models in biology at the molecular and cellular levels; application of chemical kinetics and thermodynamics to biological systems; comparison of deterministic and stochastic strategies.
All trainees are required to take a course in Responsible Conduct of Research. Some of the courses that fulfill this requirement are listed below.
BMI 738- Ethical conduct of research for data scientists: This course is designed to help data scientists, particularly those who work with biomedical data, develop their skills for making ethical decisions in their research and professional interactions. This course covers all of the NIH required topics as well as many of the suggested ones for RCR training.
Biochem 701– Professional Responsibility: Training for the practical aspects of being a scientist. Covers ethics, peer review, grant writing, science communication, career alternatives, paper writing, experimental design, research documentation, science funding, academic-private interface, scientific fraud, and more.
Nursing 802– Ethics and the Responsible Conduct of Research: Ethical issues in the design, conduct and reporting of research are examined in the context of the nature of the scientific endeavor, the structure of the research community, and professional and federal guidelines for supporting scientific integrity and controlling misconduct.
Biochem 729– Responsible Conduct of Research. Examination and discussion of key topic areas: conflicts of interest; academia and industry; mentor and mentee relationships; confidentiality, peer review and intellectual property; collaboration, authorship and publication; data management; personal, institutional and societal responsibilities; select agents.
