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Unit (2020)

Information on this page, including unit offerings, is from the 2020 academic year.

Introduction to bioinformatics and data science (BIO513)

Organisational Unit Medical, Molecular and Forensic Sciences
Credit Points 3
Availability MURDOCH: S2-internal
Teaching Timetables Murdoch S2
Description This unit aims to give students a comprehensive knowledge on the data analysis strategies and pipelines used in systems medicine. Covering techniques for transforming raw data into biologically meaningful information. Standard unsupervised (eg principal component analysis) and supervised (e.g. orthogonal partial least square discriminant analysis) multivariate data analysis techniques, and other more advanced machine learning techniques such as neural network analysis and Bayesian probability will be covered. This unit includes lectures and workshop sessions to ensure a thorough understanding of these techniques.
Unit Learning Outcomes This unit covers both exploratory techniques (such as principal component analysis, and clustering analysis) and supervised data analysis techniques that use classification (e.g. discriminant analysis, neural network, K- nearest neighbour) and regression (linear and non-linear) approaches to analyse omics datasets. Specific unit learning objectives are:
ULO1: Explain the theoretical knowledge of different data analysis techniques commonly applied in systems medicine
ULO2: Develop the specialised technical skills and able to pre-process different omics datasets
ULO3: Justify the scientific decision-making with respect to study design and able to justify appropriate application of data analysis techniques
Timetabled Learning Activities This unit has one week-long session (semester 2 usually week 2 to 4) 9am -5pm and typically involving 2 x 2hrs of lectures and 1 x 3.5hrs of workshop per day.
Unit Learning Experiences The approach to learning in this unit incorporate structured timetabled learning (workshops and lectures) and a sizeable focus on self-directed learning. These differing approaches will allow students to achieve the unit learning outcomes, in particular, to understand theoretical and practical aspects of different omics based data analysis techniques.
Assessment Assessment will comprise of two pieces of course work each focusing on pre-processing, data analysis and interpretation of metabonomic dataset (50%), and genomic and transcriptomic (50%).
Prerequisites Enrolment in Master of Systems Medicine (Research), Graduate Certificate in Systems Medicine, Graduate Diploma in Systems Medicine.
Appears in these Courses/Majors:
see individual structures for context
Graduate Certificate in Systems Medicine (GradCertSysMed)
Graduate Diploma in Systems Medicine (GradDipSysMed)
Master of Systems Medicine (Research) (MSysMed(Res))
Internet Access RequirementsMurdoch units normally include an online component comprising materials, discussions, lecture recordings and assessment activities. All students, regardless of their location or mode of study, need to have access to and be able to use computing devices with browsing capability and a connection to the Internet via Broadband (Cable, ADSL or Mobile) or Wireless. The Internet connection should be readily available and allow large amounts of data to be streamed or downloaded (approximately 100MB per lecture recording). Students also need to be able to enter into online discussions and submit assignments online.


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