Module Information
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | 1 Hours Mid Semester Test In-class Blackboard test | 40% |
| Semester Assessment | 80 Hours Analysis of biological data Essay with accompanying source code/data 3000 words plus code, data and graphs | 60% |
| Supplementary Assessment | 80 Hours Supplementary assessment Essay with accompanying source code/data 3000 words plus code, data and graphs | 60% |
| Supplementary Exam | 1 Hours Supplementary test In-class Blackboard test | 40% |
Learning Outcomes
On successful completion of this module students should be able to:
Explain scientific concepts that underpins biological data
Define a computational question aiming for knowledge discovery in biology
Read and write different types of biological data
Analyse and interpret biological data using computational methods and algorithms
Draw conclusions from the computational analysis of data
Recognise the strengths and limitations of computational methods when applied to a biological data set
Discuss the social and ethical issues in biological data mining
Brief description
This is an interdisciplinary module introducing state-of-the-art computational methods used to analyse biological data. The module focuses on string analysis which has broad applications in genomics, text mining and natural language processing. The content will be delivered within the context of DNA sequence analysis (e.g., predicting gene functions ) and health informatics (e.g., information retrieval from electronic medical records), and the module will cover a wide range of algorithms for efficient string storage, search, comparison, annotation, compression, semantics analysis and prediction. The students will be gently introduced to biological concepts and terminology with no prior knowledge required and will have the opportunity to apply their computing skills to discover new knowledge in life sciences.
Content
Applications of computational bioinformatics from association mapping and biomarker discovery to disease diagnosis and prevention
Computational methods for DNA sequence alignment, genome assembly, gene annotation and protein structure prediction
Computational methods for word tokenisation, vectorisation, semantics and sentiment analysis
Symbolic time series analysis where data from wearable sensors (e.g., smartphone or continuous glucose monitoring sensor) are transformed into strings and analysed for anomaly detection (e.g., fall detection or high blood sugar)
Shell scripting for creating data processing pipelines in Unix environment
Ethical issues surrounding retrieval and use of biological information
Module Skills
| Skills Type | Skills details |
|---|---|
| Adaptability and resilience | Interdisciplinary skills and knowledge |
| Co-ordinating with others | Practical sessions and in-class activities |
| Creative Problem Solving | Data analysis skills, algorithm and data structure skills. |
| Critical and analytical thinking | Formulating a research question and the application of computational methods to test hypotheses. |
| Digital capability | Programming and using computational tools. |
| Professional communication | Documenting code, report writing. |
| Real world sense | The applications of biological data mining |
| Reflection | Understanding the impact of biological data mining. |
| Subject Specific Skills | Using a computer and online tools. Readings from current scientific literature. |
Notes
This module is at CQFW Level 6