Outline

Overview

Biotechnological advances have given rise to an explosion of original and shared public data relevant to human health. These data, including the monitoring of expression levels for thousands of genes and proteins simultaneously, together with multiple databases on biological systems, now promise exciting, ground-breaking discoveries in complex diseases. Critical to these discoveries will be our ability to unravel and extract information from these data. In this unit, you will develop analytical skills required to work with data obtained in the medical and diagnostic sciences. You will explore clinical data using powerful, state of the art methods and tools. Using real data sets, you will be guided in the application of modern data science techniques to interrogate, analyse and represent the data, both graphically and numerically. By analysing your own real data, as well as that from large public resources you will learn and apply the methods needed to find information on the relationship between genes and disease. Leveraging expertise from multiple sources by working in team-based collaborative learning environments, you will develop knowledge and skills that will enable you to play an active role in finding meaningful solutions to difficult problems, creating an important impact on our lives.

Unit details and rules

Academic unit	Department of Medical Sciences
Credit points	6
Prerequisites ?	DATA1X01 or 48 credit points of any 1000 or 2000-level units in (ANAT, AVBS, BCMB, BIOL, BIOS, CHEM, COMP, COSC, DATA, ENVX, EXSS, FOOD, FMHU, GEGE, HPSC, HSBH, IMMU, MATH, MEDS, MICR, MIMI, PCOL, PHSI, PHYS, PSYC, QBIO, or STAT)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Exploratory data analysis, sampling, simple linear regression, t-tests, confidence intervals and chi-squared goodness of fit tests, familiar with basic coding, basic linear algebra
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Grant Parnell, grant.parnell@sydney.edu.au

Assessment

The census date for this unit availability is 2 April 2024

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Reflection 2 Written reflection	2%	Formal exam period Due date: 07 Jun 2024 at 23:59	2 pages
Outcomes assessed:
Assignment	Project report Report	15%	Formal exam period Due date: 03 Jun 2024 at 23:59	15 pages
Outcomes assessed:
Assignment	Lab report: module 1 Report	4%	Week 03 Due date: 08 Mar 2024 at 23:59	4 pages
Outcomes assessed:
Assignment	Lab report: module 2 Report	4%	Week 06 Due date: 29 Mar 2024 at 23:59	4 pages
Outcomes assessed:
Assignment	Multimedia Project	10%	Week 07 Due date: 08 Apr 2024 at 23:59	3 minutes, 500 words
Outcomes assessed:
Presentation	Oral presentation 1 Oral presentation	10%	Week 07 Due date: 08 Apr 2024 at 23:59	10 minutes
Outcomes assessed:
Assignment	Reflection 1 Written reflection	2%	Week 07 Due date: 12 Apr 2024 at 23:59	2 pages
Outcomes assessed:
Skills-based evaluation	Skill-based exam Skill based examination	35%	Week 08 Due date: 15 Apr 2024 at 16:00	2 hours
Outcomes assessed:
Assignment	Lab report: module 3 Report	4%	Week 09 Due date: 26 Apr 2024 at 23:59	4 pages
Outcomes assessed:
Assignment	Lab report: module 4 Report	4%	Week 12 Due date: 17 May 2024 at 23:59	4 pages
Outcomes assessed:
Presentation	Oral presentation 2 Oral presentation	10%	Week 13 Due date: 20 May 2024 at 23:59	10 minutes
Outcomes assessed:
= group assignment ?

Assessment summary

Skilled-based exam. Students will be given an opportunity to demonstrate individual mastery of foundation analytic skills and coding ability in an exam based setting.
Multimedia. Students will source a publicly available dataset of their choice, formulate and test appropriate scientific hypotheses and create a multimedia (pamphlet, website, Youtube video etc.) to communicate their findings.
Reproducible report. Students will identify a manuscript that uses publicly available high-dimensional biomedical data. They will generate an Rmarkdown report which reproduces the analysis in the manuscript and comment on any discrepencies or interesting extensions of the analysis.
Lab reports. Students will submit reports summarising the work they have completed in class every 3 weeks to ensure they are keeping up with the course content.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a High distinction indicates work of an exceptional standard, a Distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Mastery of topics showing extensive integration and ability to transfer knowledge to novel contexts; treatment of tasks shows an advanced synthesis of ideas; demonstration of initiative, complex understanding and analysis; work is very well presented; all criteria addressed and learning outcomes achieved to an outstanding level.

Distinction

75 - 84

Excellent achievement, consistent evidence of deep understanding and application of knowledge in medical science; treatment of tasks shows advanced understanding of topics; demonstration of initiative, complex understanding and analysis; work is well-presented; all criteria addressed and learning outcomes achieved to a superior level.

Credit

65 - 74

Confident in explaining medical science processes, with evidence of solid understanding and achievement; occasional lapses indicative of unresolved issues; treatment of tasks shows a good understanding of topic; work is well-presented with a minimum of errors; all criteria addressed and learning outcomes achieved to a high level.

Pass

50 - 64

Satisfactory level of engagement with and understanding of topic; some inconsistencies in understanding and knowledge of medical science; work is adequately presented, with some errors or omissions, most criteria addressed and learning outcomes achieved to an adequate level.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

All assignments must be submitted by the due date and quizzes and exams attended when they are scheduled. Students are expected to manage their time and to prioritise tasks to meet deadlines. Assessment items submitted after the due date without an approved extension using a special consideration or special arrangement form or request will incur penalties. Written work submitted electronically after 11.59 pm on the due date will be considered to have been submitted late. For every calendar day up to and including ten calendar days after the due date, a penalty of 5% of the maximum awardable marks will be applied to late work. The penalty will be calculated by first marking the work, and then subtracting 5% of the maximum awardable mark for each calendar day after the due date. For work submitted more than ten calendar days after the due date a mark of zero will be awarded. The marker may elect to, but is not required to, provide feedback on such work.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Importance of data: investigating biomedical and health data	Seminar (5 hr)
Week 02	Importance of data: data structure and bias	Seminar (5 hr)
Week 03	Importance of data: starting a project	Seminar (5 hr)
Week 04	Clinical data analysis: understanding your data	Seminar (5 hr)
Week 05	Clinical data analysis: analysis of variance	Seminar (5 hr)
Week 06	Clinical data analysis: regression	Seminar (5 hr)
Week 07	Omics data analysis: omics	Seminar (5 hr)
Week 08	Omics data analysis: testing in high dimensions	Seminar (5 hr)
Week 09	Omics data analysis: interpretation	Seminar (5 hr)
Week 10	Big data: Genomics in monitoring disease outbreaks	Seminar (5 hr)
Week 11	Big data: concepts behind statistical and machine learning	Seminar (5 hr)
Week 12	Big data: prediction, classification and model evaluation	Seminar (5 hr)

Attendance and class requirements

Where tutorials/workshops/laboratories have been scheduled, students should make every effort to attend and participate at the scheduled time. Students should discuss any problems with the coordinator.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. design and evaluate an appropriate modelling approach to analyse a variety of experimental designs that address different complex biomedical questions
LO2. extract, utilise and combine data from multiple public data resources
LO3. formulate scientific questions and use coding language to produce, interpret and compare numerical and graphical summaries of the corresponding complex biomedical data
LO4. justify the appropriate statistical tests to analyse biomedical data using coding language and judge the robustness and stability of the chosen tests
LO5. construct infographics to explore, interpret and communicate big data
LO6. formulate, evaluate and interpret appropriate linear models to describe the relationships between multiple variables
LO7. perform statistical machine learning using an existing classifier, and create a cross-validation scheme to calculate the prediction accuracy
LO8. identify and critique the different cross-validation strategies commonly used in the literature, and critically assess their validity
LO9. use coding to create and generate a reproducible report to communicate outcomes.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Minor changes to the weightings of the assessments including the mid-semester test have been made since this unit was last offered.

Additional information

Science student portal

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

AMED3002: Interrogating Biomedical and Health Data

Semester 1, 2024 [Normal day] - Westmead, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect