Master of Science with Specialization in Bioinformatics

Comments about Master of Science with Specialization in Bioinformatics - At the institution - Ottawa - Ontario

• Objectives
  Bioinformatics is an emerging and increasingly important scientific discipline dedicated to the pursuit of fundamental questions about the structure, function and evolution of biological entities through the design and application of computational approaches. Fundamental research in these areas is expected to increase our understanding of human health and disease which will translate to innovation in industry (i.e. drug discovery). As a field of research, it crosses traditional disciplinary boundaries such as computer science, chemistry, biology, biochemistry, engineering and the medical sciences. While individual researchers usually specialize in a particular area, bioinformaticians today must be able to appreciate significant research in other fields and therefore require an understanding of the basic principles of other disciplines. To meet this challenge Carleton University and the University of Ottawa offer a collaborative program leading to a Master of Science degree with Specialization in Bioinformatics or Master of Computer Science degree with Specialization in Bioinformatics.
• Academic title
  Master of Science with Specialization in Bioinformatics
• Course description
  Program Requirements
  
  The student is responsible for fulfilling both the participating unit requirements for the Master’s degree, and the requirements of the Collaborative Program.
  
  The minimum requirements of the collaborative program include successful completion of two required courses, and a master’s thesis on an approved bioinformatics topic.
  
  A three-credit course at the University of Ottawa is equivalent to a 0.5 credit course at Carleton University.
  
  Required courses
  
      * BIOL 5515 Bioinformatics  (0.5 credit)
      * BIOL 5517 Bioinformatics Seminar (0.5 credit)
  
  Students in programs in Biology, Computer Science, Mathematics & Statistics may use BIOL 5515 Bioinformatics to count towards degree requirements; BIOL 5517 Bioinformatics Seminar must be taken in addition to the regular seminar course.
  
  In addition, the student’s thesis committee or advisory committee may direct the student to take or audit further courses to complement the student’s background and research program.
  
  Thesis
  
  Candidates must successful complete a research thesis on a topic in bioinformatics supervised by a faculty member of the Collaborative Program in Bioinformatics.
  
  Required Courses
  
  BIOL 5515 [0.5 credit] (BNF 5106)
  Bioinformatics
  Major concepts and methods of Bioinformatics.  Topics may include, but are not limited to:  genetics, statistics and probability theory, alignments, phylogenetics, genomics, data mining, protein structure, cell simulation and computing.
  
  BIOL 5517 [0.5 credit] (BNF 6100)
  Bioinformatics Seminar
  Current topics in bioinformatics.  Students must successfully complete a presentation and written report.
  
  Other Courses
  
  BIOL 5105 (BIO 5302)
     
  Methods in Molecular Genetics
  
  BIOL 5201 (BIO 8301)
     
  Evolutionary Genetics and Computer Analysis
  
  BIOL 5409 (BIO 5306)
     
  Mathematical Modeling for Biologists
  
  BIOL 5500 (BIO 5207)
  
  Selected Topics
  
  BIOL 5501 (BIO 8100)
  
  Selected Topics in Biology I
  
  BIOL 5502 (BIO 8102)
  
  Selected Topics in Biology II
  
  BIOL 5516 
  
  Applied Bioinformatics
  
  COMP 5105 (CSI 5132)
  
  Parallel Processing Systems
  
  COMP 5306 (CSI 5100)
     
  Data Integration
  
  COMP 5307 (CSI 5101)
  
  Knowledge Representation
  
  COMP 5704 (CSI 5131)
  
  Parallel Algorithms and Applications in Bioinformatics
  
  COMP 5703 (CSI 5163)
  
  Algorithm Analysis and Design
  
  COMP 5108 (CSI 5126)
  
  Algorithms in Bioinformatics
  
  COMP 5709 (CSI 5165)
  
  Combinatorial Algorithms
  
  COMP 5706 (CSI 5387)
  
  Data Mining and Concept Learning
  
  STAT 5708 (MAT 5170)
  
  Probability Theory
  
  STAT 5709 (MAT 5171)
  
  Probability Theory II
  
  STAT 5703 (MAT 5181)
  
  Data Mining I
  
  STAT 5702 (MAT 5182)
     
  Modern Applied/Computational Statistics
  
  STAT 5600 (MAT 5190)
  
  Mathematical Statistics I
  
  STAT 5501 (MAT 5191)
  
  Mathematical Statistics II
  
  MATH 6508 (MAT 5314)
  
  Topics in Probability and Statistics
  
  MATH 6507 (MAT 5319)
  
  Topics in Probability and Statistics
  
  SYSC 5104 (ELG 6114)
  
  Methodologies for Discrete-event Modeling and Simulation
  

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