Training activity information
Details
Present the opportunities and challenges in applying machine learning for genomics
Type
Developmental training activity (DTA)
Evidence requirements
Evidence the activity has been undertaken by the trainee.
Reflection on the activity at one or more time points after the event including learning from the activity and/or areas of the trainees practice for development.
An action plan to implement learning and/or to address skills or knowledge gaps identified.
Considerations
- Applications of machine learning methods in genomics such as:
- Modelling sequencing error to enable base quality recalibration (GATK)
- Diagnosing genetics syndromes from patient photos
- Continuously adaptive systems that predict health outcomes
- Clinically safe and effective use of machine learning in genomics
- Challenges in maintaining patient safety when faced with black box systems and advantages of “explainable AI”
- National reports and strategy documents that make recommendations for adoption of machine learning technologies in the NHS
- The importance of training data and it’s selection for ML models
- Bias in ML
- Regulation and governance
- Understanding the provenance of outputs and their uncertainty
Relevant learning outcomes
| # | Outcome |
|---|---|
| # 4 |
Outcome
Summarise results of data analysis to stakeholders. |
| # 6 |
Outcome
Evaluate the potential of emerging methods in data science and the application to Clinical Bioinformatics Genomics. |