🌼 June is Bustin' Out All Over
All over the meadow and the hill
Welcome to the one hundred and twenty-first issue of Monday Morning Data Science from the Fred Hutch Data Science Laboratory. We are excited to show you what we have been working on (Fresh from the Lab), plus links that we think you would be interested in (Our Weekly Bookmarks Bar). Part of the purpose of this newsletter is to start conversations, so if you have a question or there is something you would like to share with us please let us know by responding directly to this email.
Fresh from the Lab
[Video Interview: Using Federated Learning for Collaborative AI Training] The Cancer AI Alliance (CAIA) brings together four comprehensive cancer centers plus technology and industry partners to advance cancer research by using federated learning, allowing models to learn from patient data while the data stays securely within each institution. In this video Brian Bot explains that CAIA’s goal is to democratize access to high-quality, diverse cancer data, tooling, and compute so researchers can build more generalizable AI models and uncover new insights, while emphasizing that success depends on shared mission, executive buy-in, and foundational data-plumbing work.
Our Weekly Bookmarks Bar
[Learning Resource: Bioinformatics Tutorials] Mihai Pop, computer scientist and professor, with research at the intersection of algorithms and microbiome science, has quietly assembled a full set of bioinformatics and genomic algorithms lecture notes online, spanning undergraduate through graduate-level material. If you’ve ever wanted a structured path through topics like genome assembly, sequence alignment, and algorithmic thinking for genomics, this is a great start.
As always you can contact us by replying directly to this email, or if you work within the Fred Hutch/University of Washington/Seattle Children’s Cancer Consortium you are welcome to join us on the Fred Hutch Data Slack Workspace. For more information about the Fred Hutch Data Science Lab, visit our website: https://hutchdatascience.org/. See you soon!
- The Fred Hutch Data Science Laboratory