Course Title: Advanced Topics in Bioinformatics

Part A: Course Overview

Course Title: Advanced Topics in Bioinformatics

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2307

City Campus

Undergraduate

140H Computer Science & Information Technology

Face-to-Face

 Sem 2 2006

COSC2308

City Campus

Postgraduate

140H Computer Science & Information Technology

Face-to-Face

 Sem 2 2006

Course Coordinator: Vacant

Course Coordinator Phone: +61 3 9925 2348

Course Coordinator Email: do-not-reply@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

You may not enrol in this course unless it is explicitly listed in your enrolment program summary, and you have confirmed with your program coordinator that it is an appropriate choice for your study plan.

You should have capabilities acquired in Bioinformatics


Course Description

Note: This course is not currently available. There are no plans to reintroduce it in the near future, as well.

This course presents state-of-the-art algorithms for efficient data analysis and advanced applications. Students will acquire knowledge about advanced algorithms, architectures and data structures, learn how to choose appropriate ones to solve complex problems, and be able to explain their decisions. This course builds on the capabilities acquired in Computing Fundamentals, and complements the material of the courses Bioinformatics 1.


Objectives/Learning Outcomes/Capability Development

Capabilities

Development of student graduate capabilities is an on-going process that takes place in all courses and over the period of the whole program. This course particularly addresses the following capabilities:

  • Problem solving: a range of different approaches to solving frequent problems in Bioinformatics, in particular in relation to analysis of patterns in sequences and in 3D-structures.
  • Critical analysis: ability to choose appropriate data structures and algorithms for complex tasks
  • Communication: ability to communicate, animate and explain algorithms


On completion of this courses you should be able to:

  • Select appropriate heuristics to address NP-hard problems
  • Choose appropriate algorithms and data structures to solve a complex problem
  • Compare and evaluate strengths and weaknesses of data structures and algorithms, and communicate this understanding


Overview of Learning Activities

The learning activities will include:

  • Face-to-face lectures
  • Seminar-style presentations by students

Attendance:
While a minimum attendance standard is not compulsory, non-attendance may seriously jeopardise the chances of success in this course. Clearly, non-attendance at an assessment will result in failure of that assessment. Where visa conditions apply, attendance is compulsory.


Overview of Learning Resources

A reading pack will be provided and additional reading references will be advised during class.

For extra support with study organisation, assignment planning or learning skills you may wish to contact any of the following:

Learning Skills Unit:
For appointments - ring 9925 4488 or go to Bldg 93, level 3
For drop-in, no appointment needed - go to HUB Bldg 12, level 4

CS&IT Teaching & Learning Advisors:
For appointments go to http://inside.cs.rmit.edu.au/staffbooking/ & click on Jeanette Holkner or Cecily Walker.


Overview of Assessment

Assessment for this course will consist of assignment work and class presentation.

For standard assessment details, including deadlines, weightings, and hurdle requirements relating to Computer Science and IT courses see: http://www.rmit.edu.au/compsci/cgi