Page Comparison

This idea in IP3

https://ip3.pistoiaalliance.org/subdomain/main/end/node/2055

Meeting Minutes

Meeting Minutes and Project Files

• Google drive - Best Practices Toolkit

Useful Links

• MLflow
• SAS Model Manager
• Add more here and supply links

Publication References

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Summary of Our Paper in Drug Discovery Today ("The 11 Commandments")

1. DS is not enough: domain knowledge is a must
2. Quality data (…and metadata) à quality models
3. Long-term data management methodology, e.g. FAIR for life cycle planning for scientific data
4. Publish model code, and testing and training data, sufficient for reproduction of research work, along with model results
5. Use model management system
6. Use ML methods fit for problem class
7. Manage executive expectations
8. Educate your colleagues – leaders in particular
9. AI models + humans-in-the-loop à “AI-in-the-loop” (Chas Nelson invented the term)
10. Experiment and fail fast if needed. A bad ML model that is quickly deemed worthless is better than a deceptive model
11. Maintain an Innovation Center for moonshot-type technology programs (this COE is an example of one)

Vladimir A. Makarov, Terry Stouch, Brandon Allgood, Chris D. Willis, Nick Lynch, "Best practices for artificial intelligence in life sciences research", Drug Discovery Today, Volume 26, Issue 5, 2021, Pages 1107-1110, ISSN 1359-6446, https://doi.org/10.1016/j.drudis.2021.01.017.  Free pre-print available at https://osf.io/eqm9j/
Abstract: We describe 11 best practices for the successful use of artificial intelligence and machine learning in pharmaceutical and biotechnology research at the data, technology and organizational management levels. 

Walsh, I., Fishman, D., Garcia-Gasulla, D. et al. DOME: recommendations for supervised machine learning validation in biology. Nat Methods 18, 1122–1127 (2021). https://doi.org/10.1038/s41592-021-01205-4