Computational Gene Expression

Subject 433-650 (2008)

Note: This is an archived Handbook entry from 2008.Search for this in the current handbook

Credit Points: 12.500
Level: Graduate/Postgraduate
Dates & Locations:

This subject has the following teaching availabilities in 2008:

Semester 2, - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: Thirty-six hours; Non contact time commitment 84 Hours.
Total Time Commitment: Not available
Prerequisites: Three semesters of programming experience or equivalent.
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support:


Dr Linda Stern
Subject Overview: The objective of this subject is for students to become familiar with a range of algorithms used to process biological sequences: to know a variety of techniques for extracting information from biological sequences; and to understand the appropriate application of these algorithms for different applications.

Topics covered include dynamic programming algorithms; multiple sequence alignment algorithms; hidden Markov models; sequence comparison; data mining for biological data.

Assessment: One 3-hour examination (70%) and an assignment of 2000 words equivalent (30%). Both the project work and examination component of assessment must be completed satisfactorily to pass the subject.
Prescribed Texts: None
Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

On successful completion, students should be able to:

  • Implement the basic algorithms used in processing biological sequences;
  • Select appropriate algorithms to process biological sequences;
  • Use and critically evaluate the output from software used for processing biological sequences;
  • Describe current research issues in Bioinformatics.
Related Course(s): Master of Biomedical Engineering
Master of Engineering Science(Biomedical Enginering)
Master of Software Systems Engineering
Master of Utilities Management

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