203 Biocomputing Algorithms (4 units) Winter
Instructor(s): T. Capra Prerequisite(s): Students are expected to have programming competence in a language such as Python, C, C++, or Fortran. Students should also posses a basic knowledge of statistics (undergrad. level). In addition, to take the course, all incoming for-credit students must pass a brief programming background technical assessment, conducted with a TA by Zoom. It is the students responsibility to arrange a meeting for the assessment.
Restrictions: For all iPQB students and Bioengineering students. Others require instructor approval. Activities: Direct - Lecture, Student - Lecture
Introduction to computational issues and methods used in the field of bioinformatics and computational biology. This course emphasizes the implementation, analysis, and validation of methods. It is about attacking computational problems in biology, not expert use of existing tools. Areas addressed include analytical thinking, problem decomposition, and algorithm design and implementation. Assignments will focus on the design and implementation of key bioinformatics algorithms.
206 Statistical Methods for Bioinformatics (4 units) Fall
Instructor(s): K. Pollard Prerequisite(s): Upper division course work in biological sciences including knowledge of proteins and protein structure, computer literacy.
Restrictions: None. Activities: Direct - Lecture, Direct - Project, Student - Lecture
Broad survey of bioinformatics with accompanying assignments. Topics covered include genomics, database searching, family/super-family analysis, structural genomics, complex systems, genetic circuits, and protein-protein interactions.
212 Deep Learning for Biological and Clinical Research (5 units) Winter
Instructor(s): R. Abbasi Asl Prerequisite(s): Experience with numerical computation in Python is required.
Restrictions: None. Activities: Direct - Lecture, Direct - Seminar, Direct - Discussion, Student - Lecture, Student - Seminar, Student - Project
This course will establish the foundations of deep learning through lectures, weekly seminars, and a hands-on approach in Python. We will cover the basics of regression and classification, model optimization, and neural network architectures, including autoencoders, convolutional networks, and transformers, with the use cases of these models in biological and clinical research. This is the same class as BIOENGR 212. Students should ensure they are registering with the right course number.
219 Special Topics in Bioinformatics (3 units) Fall, Spring
Instructor(s): Staff Prerequisite(s): None.
Restrictions: Priority given to first-year graduate students. Activities: Direct - Lecture, Direct - Independent Study, Direct - Conference, Student - Lecture
Each course offering will focus on the literature of a current important area of Bioinformatics. Students will be expected to read assigned papers critically before class and to present and discuss papers in class. Students will also be expected to write and present a brief research proposal based upon their reading. Topics in Molecular, Cellular, developmental, systems, and computation biology will be covered in separate course offerings.
220 Informatics Seminar (1 units) Fall, Winter, Spring
Instructor(s): R. Hernandez Prerequisite(s): None.
Restrictions: n/a Activities: Direct - Lecture, Student - Lecture
This course consists of presentation and discussion of research in quantitative biology and bioinformatics by outside speakers.
221 Informatics Rotation (1 - 8 units) Fall, Winter, Spring, Summer
Instructor(s): Staff Prerequisite(s): None
Restrictions: None Activities: Direct - Lab-Science
An introduction to the specific research currently underway within a faculty member's laboratory.
222 Student Informatics Seminar (1 units) Fall, Winter
Instructor(s): R. Hernandez Prerequisite(s): None
Restrictions: Must be a student in the Biological & Medical Informatics Graduate Program Activities: Direct - Seminar, Student - Seminar
This course gives students the opportunity to develop and polish their presentation and research skills. All second year and above BMI students present their research to other students, postdocs and faculty. Their presentations are critically evaluated and they are provided with constructive feedback regarding their discussion topic and presentation skills.
223 Critical Topics in Biomedical Informatics (1 units) Fall, Winter, Spring
Instructor(s): J. Wells Prerequisite(s): None.
Restrictions: None. Activities: Direct - Seminar, Student - Seminar
Critical review of published scientific papers from scholarly journals, including comprehension, analysis and evaluation of published scientific data.
250 Research (4 - 8 units) Fall, Winter, Spring, Summer
Instructor(s): Staff Prerequisite(s): None
Restrictions: None Activities: Direct - Project
In this course, students will work together with a primary research advisor to select a research question and design a project workplace that will be carried out by the student. Through this activity, the student will gain experience in research strategy, learn techniques associated with modern biomedical research, and practice how to interpret results. At the conclusion of the course, the student will present on their progress.
311 Curricular Development and Academic Leadership (0.5 - 4 units) Fall, Winter, Spring
Instructor(s): R. Hernandez Prerequisite(s): None
Restrictions: BMI students only Activities: Direct - Seminar, Direct - Workshop, Direct - Lab-Science, Direct - Discussion, Student - Seminar, Student - Workshop, Student - Lab-Science, Student - Discussion
The Curricular Development & Academic Leadership course will offer training and leadership to prepare graduate students in scientific leadership roles in the classroom and beyond. Students will have a hands-on approach to structuring and executing a curriculum. Students must submit an application prior to course enrollment.