[panabee]

Agreed. There is deep potential for ML in healthcare. We need more contributors advancing research in this space. One opportunity as people look around: many priors merit reconsideration.

For instance, genomic data that may seem identical may not actually be identical. In classic biological representations (FASTA), canonical cytosine and methylated cytosine are both collapsed into the letter "C" even though differences may spur differential gene expression.

What's the optimal tokenization algorithm and architecture for genomic models? How about protein binding prediction? Unclear!

There are so many open questions in biomedical ML.

The openness-impact ratio is arguably as high in biomedicine as anywhere else: if you help answer some of these questions, you could save lives.

Hopefully, awesome frameworks like this lower barriers and attract more people.

[govideo]

I'd love to hear more of our thoughts re open questions in biomedical ML. You sound like you have a crisp, nuanced grasp the landscape, which is rare. That would be very helpful to me, as an undergrad in CS (with bio) trying to crystalize research to pursue in bio/ML/GenAI.

Thank you.

[panabee]

Thanks, but no one truly understands biomedicine, let alone biomedical ML.

Feynman's quote -- "A scientist is never certain" -- is apt for biomedical ML.

Context: imagine the human body as the most devilish operating system ever: 10b+ lines of code (more than merely genomics), tight coupling everywhere, zero comments. Oh, and one faulty line may cause death.

Are you more interested in data, ML, or biology (e.g., predicting cancerous mutations or drug toxicology)?

Biomedical data underlies everything and may be the easiest starting point because it's so bad/limited.

We had to pay Stanford doctors to annotate QA questions because existing datasets were so unreliable. (MCQ dataset partially released, full release coming).

For ML, MedGemma from Google DeepMind is open and at the frontier.

Biology mostly requires publishing, but still there are ways to help.

After sharing preferences, I can offer a more targeted path.

[govideo]

ML first, then Bio and Data. Of course, interconnectedness runs high (eg just read about ML for non-random missingness in med records) and that data is the foundational bottleneck/need across the board.

Interesting anecdote abt Stanford doctors annotating QA question!

Each of your comments get my mind going... I'm going to think about them more and may ping you on other channels, per your profile. Thanks!

[panabee]

More like alarming anecdote. :) Google did a wonderful job relabeling MedQA, a core benchmark, but even they missed some (e.g., question 448 in the test set remains wrong according to Stanford doctors).

For ML, start with MedGemma. It's a great family. 4B is tiny and easy to experiment with. Pick an area and try finetuning.

Note the new image encoder, MedSigLIP, which leverages another cool Google model, SigLIP. It's unclear if MedSigLIP is the right approach (open question!), but it's innovative and worth studying for newcomers. Follow Lucas Beyer, SigLIP's senior author and now at Meta. He'll drop tons of computer vision knowledge (and entertaining takes).

For bio, read 10 papers in a domain of passion (e.g., lung cancer). If you (or AI) can't find one biased/outdated assumption or method, I'll gift a $20 Starbucks gift card. (Ping on Twitter.) This matters because data is downstream of study design, and of course models are downstream of data.

Starbucks offer open to up to three people.