BMS5307 · Research advanced case studies in bioinformatics

Overview

In this unit you will further your ability to apply critical scientific reasoning to the examination of bioinformatics datasets. You will analyse large and inherently complex data, evaluate methodological rigour and data interpretation. Emphasis will be placed on synthesising best practices and developing skills in critiquing shortcomings in published proteomics and AI-driven protein design studies, alongside appraising choices in experimental setup and analysis workflow. This unit is designed to equip you with practical data analysis and evaluation, skills which are highly valued in bioinformatics professional settings.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Evaluate the role of proteomics alongside genomics and transcriptomics in the context of biological analysis.
2. Compare data-driven and physics-based approaches to protein modelling and design.
3. Interpret proteomics and AI-drive protein design data to generate and evaluate biological hypotheses.
4. Critically evaluate research design, reproducibility, and the strengths and limitations of proteomics approaches.
5. Design and justify testable hypotheses, and develop robust computational workflows for investigating proteome-related research questions.
6. Communicate research findings effectively to academic and professional stakeholders.
7. Critically reflect on skill development and professional practice relevant to contemporary bioinformatics contexts.

Workload

Average of 12 hours teacher-directed learning/week (on-campus workshops, online learning materials) plus 12 hours student-directed learning Total per week = 24 hours

Student experience, not official rules. Nothing here changes what the Handbook says.