Courses - Students are required to complete 6 of the following courses: 

    Core Courses (mandatory*):

    MBB 505/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.

    MBB 659/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.

    MBB 741 | BIOINFORMATICS
    This course introduces the history of bioinformatics, classic algorithms used in the field, common methods of macromolecule analysis (ie within

    areas of sequence alignment, structure analysis, phylogenetic analysis, etc.) and an introduction to bioinformatics-related programming and

    database connectivity.

   

    CMPT 881 | ALGORITHMS FOR MOLECULAR COMPUTATIONAL BIOLOGY
    In this course we will study algorithms for the acquisition and analysis of information from DNATopics Sequence similarity; Sequence 

    alignment   and multiple sequence alignment; String alignment and algorithms for optimal alignment; Proteins and folding; Physical Mapping;

    Phylogenies.

   

    CMPT 711 | BIOINFORMATICS ALGORITHMS - may be a substitute for CMPT 881
    This is an introductory level graduate course on fundamental computational techniques which have been successfully applied to key problems

    in bioinformatics. Particular problem areas of interest include sequence alignment and search, motif discovery, molecular structure prediction,

    phylogenetics, biomolecular interactions and cellular networks. We will cover various computational tools ranging from ones which are

    combinatorial in nature, such as dynamic programming, index structures, approximation algorithms, and randomized algorithms to those which

    are statistical such as expectation maximization and Gibbs sampling.

    CPSC 545 | ALGORITHMS FOR BIOINFORMATICS - may be a substitute for CMPT 881

    This graduate level course in 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 course-work 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.   Note: CPSC 445 may be

    substituted for CPSC 545 if the student does not have a strong computational background.

    * 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 enrollement in graduate school.

     Elective Courses**:

CMPT 419 (cross-listed with CMPT 829)| BIOMEDICAL IMAGE COMPUTING
This course is designed to give students the knowledge needed to understand, develop,  and use software and algorithms on medical image data, to extract useful clinical information. It may be viewed as a course in image processing and computer vision  adapted to 3D (volumetric) and more complex medical images (such as MRI or CAT scans), with health-related application. Details at: http://www.cs.sfu.ca/~hamarneh/419_829.html


CMPT 705 | DESIGN AND ANALYSIS OF ALGORITHMS

CMPT 726 | MACHINE LEARNING

Machine Learning is the study of computer algorithms that improve automatically through experience. It is one of the most exciting aspects of artificial intelligence, and is the basis for many of its industrial applications. It is the preferred framework for many applications, such as face detection (auto-focus in your digital camera), hand-written digit recognition, speech recognition, and credit card fraud detection.

CMPT 741 | DATA MINING
Covers essential techniques for searching and mining large databases, in particular, biological databases, text databases and business databases. Topics: database systems, association analysis, classification and prediction, cluster analysis, searching and mining sequence & multidimensional data, and their applications.

CMPT 880 | MEDICAL IMAGE ANALYSIS

This course focuses on discussing recent research papers on medical image analysis., including topics on medical imaging, image processing/ filtering, image segmentation, image registration and shape modeling, in the context of different applications such as computer aided diagnosis and statistical shape analysis. Details at: http://www.cs.sfu.ca/~hamarneh/880.html

CPSC 304 | INTRODUCTION TO RELATIONAL DATABASES
Focus is relational databases, dealing with relational database design, relational database languages, and concepts related to the transaction processing layer (top layer) of a database management system (DBMS).

CPSC 445 | ALGORITHMS IN BIOINFORMATICS

Bioinformatics involves the application of computational methods to answer or provide insight on questions of molecular biology. This course provides an introduction to the design and analysis of algorithms for bioinformatics applications.

CPSC 504 | DATABASE DESIGN
Organizing information as relations. Information retrieval through queries against relations. Storing relations as data. Efficient storage and retrieval of data needed by queries. Reliability integrity and security considerations in database design.

HCEP 511 | CANCER EPIDEMIOLOGY
Collection and analysis of epidemiological data on cancer; occupational and other risk factors,; analytic techniques and mathematical modeling relevant to oncology.

CPSC 53A | TOPICS IN ALGORITHMS AND COMPLEXITY - BIOINFORMATICS
This course introduces algorithms and their application in bioinformatics Topics include sequence alignment, phylogenetic tree reconstruction, prediction of RNA and protein structure, gene finding and sequence annotation, gene expression, and biomolecular computing. A solid understanding of principles for design and analysis of algorithms. Some assignments will involve use and extension of software tools, and others will involve written studies of algorithms and their analysis.

MATH 561 | MATHEMATICAL BIOLOGY

MATH 612D | TOPICS IN MATHEMATICAL BIOLOGY - MATHEMATICS OF INFECTIOUS DISEASES AND IMMUNOLOGY


MBB 823 | PROTEIN STRUCTURE AND FUNCTION: PROTEOMIC BIOINFORMATICS
Transition state theory; specificity in enzyme catalyzed reactions; use of recombinant DNA techniques to describe and modify enzyme catalysis, the function of enzymes in organic solvents, and the development of new catalytic activities through monoclonal antibody techniques.

MBB 831 | MOLECULAR EVOLUTION OF EUKARYOTE GENOMES
Examination of the dynamics of change in eukaryotic nuclear, mitochondrial, and chloroplast genome structure and organization including mechanisms of gene conversion, transposition, and duplication. Consideration of the origin and function of intron, satellite, and repeated DNA sequences.

MBB 835 | GENOMIC ANALYSIS
Topics include: structure and function of the genome with emphasis on genome mapping and sequencing projects, and computational methods for genomic sequence analysis comprising: methods in genomic research, construction of physical genomic maps, ESTs - use and purpose; Sequencing strategies: ordered vs. random; high throughput sequencing; Collection and assembly of data; Gene finding (prediction of genes from DNA sequence; Annotation and release of data; Comparative Genomic analysis; Comparative Genomic analysis; Functional genomics; Genome organization; Future directions.

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.

STAT 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.

STAT 802 | MULTIVARIATE ANALYSIS

STAT 805 | NON-PARAMETRIC STATISTICS AND DISCRETE DATA ANALYSIS

STAT 890 | STATISTICS SELECTED TOPICS - BIOMETRICAL GENETICS

PATH 531/MEDG 521 | MOLECULAR AND CELL BIOLOGY OF CANCER
This course focuses on molecular and cell biology of cancer and consists of a series of lectures reviews combined with discussions and presentations by students on the topics selected by the instructors. Emphasis will be on students' presentations and discussion.

**This is not an exhaustive list of electives - more are being developed every term and will be available to students when they register.
 
top British Columbia Cancer Research Centre | British Columbia Cancer Agency | Genome Sciences Centre | University of British Columbia | Simon Fraser University

(c) 2002 Training Program in Bioinformatics for Health Research at UBC, SFU and BCCA