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
Reflective practice guidance
The guidance below is provided to support reflection at different time points, providing you with questions to aid you to reflect for this training activity. They are provided for guidance and should not be considered as a mandatory checklist. Trainees should not be expected to provide answers to each of the guidance questions listed.
Before action
- What are some potential applications of machine learning in genomics? What are the current limitations and challenges?
- How will you gain a broader understanding of the potential and limitations of machine learning in genomics? How will you develop your presentation skills in discussing emerging technologies? What is your current knowledge of machine learning principles and their application in bioinformatics?
- Will you research current literature on machine learning applications and challenges in genomics? Will you plan the structure and content of your presentation? Have you discussed the scope and focus of your presentation with your training officer? What potential challenges might you face in presenting this topic? How do you feel about discussing emerging technologies?
In action
- What specific opportunities for machine learning in genomics are you focusing on in your presentation? What are the key challenges associated with its application in this field? How are you structuring your presentation to provide a balanced view?
- Are you gathering relevant examples and evidence to support your points about opportunities and challenges? Are you organising your presentation in a logical manner? Are you considering the audience and their likely level of familiarity with machine learning?
- As you prepare, are you refining your arguments and ensuring a clear distinction between opportunities and challenges? Are you considering potential questions or counterarguments from the audience?
On action
- What opportunities and challenges did you highlight in your presentation on applying machine learning for genomics? How did you structure your presentation?
- What specific examples of opportunities and challenges in machine learning for genomics did you research and present? How did you balance the discussion of potential benefits with the associated difficulties and limitations? What were the key messages you aimed to convey to your audience? How effective do you think you were in delivering these messages? How did your preparation and delivery (‘reflect-in-action’) shape the impact of your presentation? How does understanding the opportunities and challenges of machine learning in genomics relate to the future of clinical bioinformatics?
- What areas of machine learning in genomics do you want to explore in more depth? How can you improve your presentation skills when discussing emerging technologies? What specific actions will you take to enhance your understanding and ability to present on this topic? What resources or support would be beneficial for further developing your knowledge of machine learning in genomics and your presentation skills?
Beyond action
- Have you encountered further examples of the application of machine learning in genomics since this training activity? Has your perspective on its opportunities and challenges evolved?
- Have you discussed the potential of machine learning with colleagues? Did your understanding from this training activity inform these discussions?
- How might your awareness of the opportunities and challenges of machine learning in genomics influence your approach to future research or development projects?
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. |