| Unit name | Practical Bioinformatics and Machine Learning |
|---|---|
| Unit code | BIOLM0050 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Jon Lees |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Biological Sciences |
| Faculty | Faculty of Life Sciences |
Why is this unit important?
Biology is increasingly becoming a data science, with many large-scale information rich datasets and sophisticated analysis tools becoming available. Recent trends in bioinformatics include emerging Deep Learning technologies replacing established bioinformatics tools. This unit will give you a grounding in many of the practical skills you need as a bioinformatician including key resources and tools, not yet covered in other modules. It will give you the chance to consolidate your skills (e.g. in Python) in an applied setting. The unit will give you an initial grounding in ML techniques where you will learn the strengths of different methods, how to train an ML model and pitfalls to watch out for when doing so. Along with various classic bioinformatics research challenges you will learn of emerging trends such as AI first therapeutic design and medicine.
How does this unit fit into your programme of study
This compulsory unit in Teaching Block 2 will help consolidate many of the skills you have learnt in TB1 modules (e.g. coding, Structural Biology, etc.). Early in this unit you will be given a grounding in key resources and tools.
In practical’s you will get to use these key bioinformatics web and python tools. As we move further into the unit you will be introduced to key ideas of machine learning and how to effectively develop and apply these tools. The material studied will provide useful for your research projects.
An overview of content
Within the unit, a series of lectures and associated computer practical’s will be given. The focus at the beginning of the module will be on key resources and tools available not covered elsewhere (e.g. in the field of network biology and protein functional analysis). You will engage with these tools in more detail in practical’s helping you to consolidate bioinformatics skillsets in an applied bioinformatics context.
You will be trained in the fundamental ML methods that are key to modern data-science and shown how to use these in a bioinformatics context. You will learn how to train your own ML models and deep learning approaches and cover how and why these are taking over much of bioinformatics as state of the art.
After this unit, you will be more confident in a wide range of key bioinformatics areas with a more rounded and complete skillset and support your final project and future career.
Learning Outcomes:
1. Understand the go to web-resources tools and databases to use for several classic bioinformatics challenges including basic and translational research.
2. Code up solutions to several common real world bioinformatics scenarios across various modalities.
3. Understand the fundamentals of Machine Learning and effectively design, train and assess your own models
The unit will be delivered through online preparation material and in-person sessions that will be a mixture of short lectures followed by individual exercises with computers. Blackboard will be used to engage you with the unit content.
The lectures and practicals will prepare you for a final summative assessment at the end (100%) integrating all the learning objectives in a final mini-report on a translational bioinformatics setting.
The summative assessment is an individual assignment, so if you are unable to submit due to extenuating
circumstances or pass at the first attempt, you may be allowed to work with a new topic and resubmit with an
agreed revised deadline.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. BIOLM0050).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
