Drafting 1-pager to ensure we all have a clear direction

[malvikasharan]

Revised target audience, learning objective and plans

Target audience: Experimental biologists and biomedical research communities, with a focus on two key professional/career groups

1. Senior Group leaders without any prior experience with Data Science and AI - interested in understanding the potential additionality and application of AI to their areas of expertise
2. Post doc/Lab scientists – next generation senior leaders - interested in additionality, but also the group more likely to benefit from tools to equip them with the requirements to enable the integration of computational science into biomedical science

Content: Two masterclasses will provide an overview on the potential additionality of AI/ML to life science disciplines, and to build a shared understanding of good practice principles to facilitate the integration and reproducibility of computational data science, into these areas.

Masterclasses:

• Part 1: Introduction AI in life science (Machine Learning, Deep Learning, AI, examples)
• Part 2: Getting ready for applying AI in their projects (computational project management, resources)

Action points for the project members

• Development team to share the goals and objectives with everyone
• Engage with target audience - Senior Group Leaders to refine content - James Fleming and Rebecca to coordinate for the Crick scientists

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Assess if content will contribute, in part, towards the following gaps identified recently by the Crick Data Science group

1. Foundational: Introduction to machine learning including:

• Intro to different algorithm types and how to choose what to use
• Splitting datasets appropriately and avoiding leakage.
• Problem of imbalanced data sets and of overfitting
• Statistics to evaluate output (precision-recall etc)
• Pointers to where to start with different approaches

2. Specific/specialist topics: Details requested

3. Computational project management/supervising computational projects:

The underlying theme for all this is how to make computational projects transparent, reproducible etc. The goal would be a framework for a GL to feel more confident supervising a computational biologist. This course could include:

• Organising a project: Practical directory and file structures for comp projects
• Metadata: Use relevant examples to the Crick (e.g. genomics and imaging)
• Intro to Github and importance of version control
• Intro to markdown and Jupyter notebooks

[malvikasharan]

Shared Google doc circulated via email for comments: https://docs.google.com/document/d/1DHRaXtslAGq5FGedbAGWkp4D2hmvzqt470b5e-90mC4/edit#