The deluge of data from our newfound ability to sequence genomes and visualise molecules from thousands of cells and individuals will revolutionise healthcare. Computational solutions, from distributed computing to deep learning, are increasingly essential to biomedical progress. While the transformation at the interface of high-throughput experimentation and computer science is underway, biomedical research is generating data at a faster pace than we are able to process and interpret it. This presents an unheralded opportunity for motivated students to become the new leaders of the computational biology revolution.
This course is an opportunity for students with diverse disciplinary backgrounds — including but not limited to computer science, engineering, statistics, biology, physics and mathematics — to work on real-world biomedical problems using computational methods under the supervision of top researchers at the John Curtin School of Medical Research (JCSMR). This course will embed students into multidisciplinary teams to identify and work on applications of innovative computational technologies to biomedicine.
This course can be taken as either 6 or 12 units. Students who take the 12 unit version are expected to undertake more in-depth and sustained study.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Plan and pursue a guided investigation and evaluation of computational methods to address biomedical research questions.
- Systematically apply relevant theory and methods and draw evidence-based conclusions to biomedical problems using computational technologies.
- Explain concepts in computational biology and develop a translational application.
- Demonstrate accurate and efficient use of computational methodologies to solve biomedical problems.
- Demonstrate capacity for scientific reasoning through the application of basic concepts in computational biology and their communication to academic audiences.
Work Integrated Learning
Projects
Students may engage with WIL partners (internal/external) as a component of the course.
Other Information
Admission to this course is subject to availability of a suitable supervisor and approval from the Course Convener.
Further details about potential supervisors and projects can be found via the JCSMR website: Talo Computational Biomedicine Courses | The John Curtin School of Medical Research (anu.edu.au)
Students must complete the Research Project Summary Form which can be obtained from the JCSMR HDR Student Administration Team by email (jcsmr.hdr.sa@anu.edu.au) and submitted via the following link: https://students.science.anu.edu.au/program-admin/requesting-permission-enrol
There is a financial award available from the JCSMR Talo Computational Biology Talent Accelerator.
Selection for an award will be based on academic merit.
Indicative Assessment
- Project Proposal (10) [LO 1,4]
- Software Output (20) [LO 1,2,4]
- Final Report (50) [LO 1,2,3,4,5]
- Oral Presentation (20) [LO 3,5]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
This course is available as 6 or 12 unit course. For each 6 units of enrolment, the expected minimum workload will consist of approximately 130 hours of total student learning time made up of independent student research, reading and writing. Students are expected to maintain regular contact with their supervisor and attend all supervisory meetings.
The expected workload if taken as a 6 unit course will consist of approximately 130 hours throughout the semester including approximately 65 hours of team meetings, event preparation and self-directed study, including developing skills in developing and/or using software tools, and 65 hours of computational design and data analysis. Students are expected to actively participate and contribute towards group scientific discussions, presentations in group meetings, and attend school seminars.
Inherent Requirements
No specific inherent requirements have been identified for this course.
Requisite and Incompatibility
You will need to contact the John Curtin School of Medical Research to request a permission code to enrol in this course.
Prescribed Texts
None
Preliminary Reading
An Introduction to Bioinformatics Algorithms. N.C. Jones & P. A. Pevzner. MIT Press. 2004 (https://books.google.com.cu/books?id=p_qzpkNVcUwC)
Mathematics for Machine Learning. Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press (https://mml-book.github.io/)
Assumed Knowledge
Sufficient knowledge and experience in the development of software for data analysis will be required for this course. It is recommended to have successfully completed one or more courses on algorithms, programming, and data analysis, such as:
COMP1730 Programming for scientists
COMP1100 Programming as problem solving
MATH3407 Bioinformatics and Biological Modelling
BIOL2202 Experimental design and analysis in biology
BIOL3157 Bioinformatics and its applications
COMP3600 Algorithms
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 to 12 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
| 7.00 | 0.14583 |
| 8.00 | 0.16667 |
| 9.00 | 0.18750 |
| 10.00 | 0.20833 |
| 11.00 | 0.22917 |
| 12.00 | 0.25000 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2024 | $880 per unit |
- International fee paying students
| Year | Fee |
|---|---|
| 2024 | $1180 per unit |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 8662 | 22 Jul 2024 | 29 Jul 2024 | 31 Aug 2024 | 25 Oct 2024 | In Person | N/A |
| 8663 | 22 Jul 2024 | 29 Jul 2024 | 31 Aug 2024 | 25 Oct 2024 | In Person | N/A |
