This section contains the courses offered at UBC. Students can also take courses from SFU after completing the Western Deans’ Agreement form (contact program coordinator for more details). Click here for information about SFU’s  MBB courses and click here for information about SFU’s CS courses..

M.Sc. students are required to complete a total of 6 courses. All mandatory courses have to be completed, and students pick additional electives to complete the 18 course credits required.  If a student wishes to transfer to a PhD, then only 3 courses (9 credits) are needed, with a minimum of 80% grade in each course.  Completion of two professional development courses is also required.

There is no course requirements for Ph.D. students, except the completion of 2 professional development courses.

Courses:

Core Courses (mandatory*):

*BIOF 520 | PROBLEM BASED LEARNING IN BIOINFORMATICS
The problem-based learning course will develop students’ ability to exchange ideas in small groups focused on real but simplified problems in bioinformatics. Problems will be carefully selected to cover all aspects of bioinformatics research. The core curriculum is identical during the first year for post-graduate diploma and for master’s students. The SFU equivalent is MBB 505.

*BIOF 501A | SPECIAL TOPICS IN BIOINFORMATICS
This discussion-based Bioinformatics course will expose students to the latest developments in Bioinformatics analysis and algorithms. It will run in conjunction with the VanBug Seminar Series, in which the students will have the opportunity to meet and discuss their work with guest speakers, both local and international scientists. The SFU equivalent for this course is MBB 659.

Suggested Electives:

CPSC 445 | ALGORITHMS FOR BIOINFORMATICS – for students who do not have strong computational background
This undergraduate level course offered in UBC computer-science that focuses on the algorithms that are currently in Bioinformatics. e.g. sequence alignment, gene prediction and sequence annotation, RNA and protein structure prediction and phylogenetic analysis. The aim of this course is to give you detailed understanding of the existing algorithms and to prepare you to develop you own applications and algorithms. The course is meant to be very interactive in style and will involve coursework on projects. You should be comfortable with basic mathematical reasoning, have a good understanding of the main principles of molecular biology and be confident programming in a higher-level language such as C, C++ or Java. Due to the interactive nature of the course, enrollment is restricted to a small number of dedicated students.

BIOF 540| STATISTICAL METHODS FOR HIGH DIMENSIONAL BIOLOGY
This course will cover quantitative problems arising from current research. We focus on areas in which a statistical approach provides a powerful tool for separating signal from noise. Students will learn to translate genomic research questions into well-defined computational problems. Solutions and algorithms are found which are both theoretically sound and practical to implement. Selected topics: gene expression analysis, analysis of tissue and protein arrays, sequence alignment and comparison, Hidden Markov Models.

* If you have already taken any of these courses as an undergraduate or have taken equivalent material at another University, you are not required to repeat the material, rather choose an additional elective to make up the requirement of 6 courses needed for graduate studies (18 credits). Please note that University policy specifies that no course credit can be awarded to a student towards graduate studies credits for courses taken before enrollment in graduate school.

Elective Courses**

BIOF 548A| DIRECTED STUDIES IN BIOINFORMATICS

CPSC 340 | MACHINE LEARNING AND DATA MINING
Models of algorithms for dimensionality reduction, nonlinear regression, classification, clustering and unsupervised learning; applications to computer graphics, computer games, bio-informatics, information retrieval, e-commerce, databases, computer vision and artificial intelligence.

CPSC 410| ADVANCED SOFTWARE ENGINEERING
Specification, design, construction and validation of multi-version software systems.

CPSC 420 | ADVANCED ALGORITHMS DESIGN AND ANALYSIS
The study of advanced topics in the design and analysis of algorithms and associated data structures. Topics include algorithms for graph-theoretic; algebraic and geometric problems; algorithms on non-sequential models; complexity issues; approximation algorithms.

CPSC 422| INTELLIGENT SYSTEMS
Principles and techniques underlying the design, implementation and evaluation of intelligent computational systems. Applications of artificial intelligence to natural language understanding, image understanding and computer-based expert and advisor systems. Advanced symbolic programming methodology.

CPSC 500| FUNDAMENTALS OF ALGORITHM DESIGN AND ANALYSIS
Graduate level course.

CPSC 502| ARTIFICIAL INTELLIGENCE I
Graduate level course.

CPSC 530P| TOPICS IN INFORMATION PROCESSING-SENSORIMOTOR COMPUTATION
Graduate level course.

CPSC 536H | TOPICS IN ALGORITHMS AND COMPLEXITY – EMPIRICAL ALGORITHMS
Graduate level course.

MEDG 505 | GENOME ANALYSIS
Investigation of genetic information as it is organized within genomes, genetic and physical map construction, sequencing technologies, gene identification, database accessing and integration, functional organization of genomes from contemporary, historic and evolutionary perspectives.

MEDG 520 | ADVANCED HUMAN MOLECULAR GENETICS
Genetic variation, genome analysis, cloning of genes for diseases and normal functions, mutation detection, animal models of human genetic disease.

MEDG 521| GENETICS AND CELL BIOLOGY OF CANCER
Focuses on molecular and cell biology of cancer through a series of lectures, reviews, student presentations and discussion.

MEDG 530| HUMAN GENETICS
Human Mendelian and non-Mendelian inheritance and clinical applications of genetics.

MEDG 535| GSTAT 536A | STATISTICAL THEORY FOR THE DESIGN AND ANALYSIS OF CLINICAL STUDIESENETICS AND ETHICS
This course is intended to serve the diverse needs of genetic counseling students, research graduate students in genetics, genetic residents and clinical fellows, other health professional students, and graduate students from other sciences and humanities.

STAT 521B | TOPICS IN MULTIVARIATE ANALYSIS

STAT 536A | STATISTICAL THEORY FOR THE DESIGN AND ANALYSIS OF CLINICAL STUDIES

STAT 538A | GENERALIZED LINEAR MODELS

**This is not an exhaustive list of electives – more are being developed every term and will be available to students when they register. Students are highly encouraged to check out courses listed under SFU and UBC, or talk to professors for recommendations. To register for courses at SFU, please speak with the Bioinformatics graduate coordinator.