2023/24 Taught Postgraduate Programme Catalogue
MSc Precision Medicine: Genomic Data Science
|Programme code:||MSC-BS/PMGDS||UCAS code:|
|Duration:||12 Months||Method of Attendance:||Full Time|
|Programme manager:||Dr Mark Iles||Contact address:||M.M.Iles@leeds.ac.uk|
Total credits: 180
BSc First Class or 2:1 or equivalent in a relevant scientific discipline which would normally be one of the biological sciences or natural sciences. Subject to University regulations, MBChB or BDS students who had completed 3 years of study would be eligible to intercalate. While the course does not assume any prior knowledge of statistics, we require that students demonstrate their aptitude for statistics from either undergraduate teaching in statistics/mathematics, an A-level (or equivalent) in mathematics or other relevant experience.
- English language requirement: IELTS 6.5 overall, with no less than 6.0 in any single component or equivalent (see website)
School/Unit responsible for the parenting of students and programme:
School of Molecular and Cellular Biology
Examination board through which the programme will be considered:
School of Molecular and Cellular Biology (Masters examination board)
Relevant QAA Subject Benchmark Groups:
Subject benchmark statements for biological sciences Masters programmes do not exist. However, students will be expected to demonstrate the characteristics embodied in the QAA Qualifications Frameworks level descriptors for Masters degrees (Characteristics Statement - Masters Degrees (qaa.ac.uk))
The Precision Medicine: Genomic Data Science programme is distinctive because it allows students to develop core quantitative and computational skills that underpin the analysis and interpretation of the huge volume of large biomedical data sets now being generated. Students will learn fundamental skills in data science such as coding in R and Python, as well as basic and advanced statistics, including regression, classification and machine learning. Students will at the same time learn the more specialised techniques used for analysis of genomic data, including RNASeq analysis, bioinformatics and genetic epidemiology, as well as understanding the biology and technology that underlie such data. Finally students will learn more broadly about the ways that such data are used in clinical practice across a range of diseases including cancers and autoimmune diseases, as well as gaining an understanding of clinical trials. Thus students will not only be able to engage in the types of analysis of the human genome that are needed now but also have the fundamental skills that they need to be ready for future developments in the field. This is an emerging and rapidly expanding and developing area that requires scientists to be able to both understand and drive this area forward. This programme is distinctive in that there are no MSc courses currently available that emphasise the development of such skills (analytical and computational) in the context of precision medicine. The programme is also distinctive in that it is interdisciplinary drawing on expertise across the three Faculties to provide a rich learning experience for the students. Distinctiveness will be further enhanced through case studies delivered by external speakers from industry and from within the NHS to illustrate commercial applications arising from this work and impact on patients. In particular we have incorporated an “industry awareness” day into our Analytical skills in Precision Medicine module, and are compiling a list of external volunteer participants/speakers.
The academic content of the programme concentrates on developing an understanding of how the mechanisms underpinning disease biology are elucidated through the use of large data sets and to develop the skills to be able to utilise these data themselves in the drive to facilitate precision medicine; enabling the development of new therapies, aiding earlier diagnosis and selection of optimal treatment regimes for patients.
Computational and analytic capabilities will be developed through students actively engaging with real-life data sets generated through research conducted by academics at Leeds. This practical training will culminate in a substantial research project providing further in-depth specialisation and experience in human genomics and data analytics. Overall, students will develop computational and analytical skills so that they can not only analyse data but develop the tools to do this.
Students will be exposed to cutting-edge methodologies including predictive analytics, machine learning and artificial intelligence to understand how big data and data analytics contribute to precision medicine. Case studies will be used to illustrate these approaches, some of which will be delivered by external speakers from industry and within the NHS.
Students will receive a solid grounding in a range of transferable skills valued by employers including teamwork, project-work, and in the legal, ethical and professional guidelines that are relevant to research data use.
Students will benefit the inter-disciplinary nature of the programme, experiencing first-hand the integration of academic delivery by staff from the biomedical, computational and mathematical sciences. Students will be able to attend seminars delivered through the Leeds Institute for Data Analytics, thus broadening their exposure and understanding of the role of big data across disciplines and providing an o pportunity for networking.
Year1 - View timetable
[Learning Outcomes, Transferable (Key) Skills, Assessment]
Candidates will be required to study the following compulsory modules:
|BIOL5178M||High-Throughput Technologies||15 credits||Semester 1 (Sep to Jan)|
|BIOL5210M||Biopharmaceutical Development: Clinical||15 credits||Semester 2 (Jan to Jun)|
|BIOL5327M||Analytical Skills in Precision Medicine||15 credits||Semesters 1 & 2 (Sep to Jun)|
|BIOL5352M||Research Project: Genomics and Analytics||60 credits||1 Apr to 31 Aug|
|EPIB5032M||Introduction to Genetic Epidemiology||15 credits||Semester 2 (Jan to Jun)|
|MATH5741M||Statistical Theory and Methods||15 credits||Semester 1 (Sep to Jan)|
|MATH5743M||Statistical Learning||15 credits||Semester 2 (Jan to Jun)|
|MEDM5151M||Big Data and Rare and Common Disorders||15 credits||Semester 1 (Sep to Jan)|
|MEDM5231M||Cancer Drug Development||15 credits||1 Jan to 30 Apr|
Last updated: 21/03/2023 10:09:40
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