We're cooked.

Jack Saying what amounts to — nobody does this, I don't personally do this, this is not a problem, people who do this are bad scientists — abrogates responsibility for understanding the effect on the field's methodology and reliability. Without that, there's no field in the long term.

There's a lot of excitement about foundation models and their ability to learn biology 🧬💻 But current tools for perturbation prediction perform worse than simple linear models! We need more careful benchmarking to make progress. biorxiv.org/content/10.110…

biorxiv.org/content/10.110… Nice benchmark of single cell "foundation models" (scGPT, scFoundation) and GEARS (a GNN model) further hyped as "virtual cell models" against linear baselines on perturbation prediction. Long-story short: they can't beat the linear baselines. 1/

Help ! Do you know how to get cloud storage for an academic lab that benefits from non-profit pricing ?

Our group of #ImageForensics experts Cheshire , Mu Yang, Ph.D., Matthew Schrag, and myself are currently posting the problems we found in these papers. We have now posted 118 of Dr. Masliah's paper onto PubSmear. Follow our progress here: pubpeer.com/search?q=%22El…

It's a weird and beautiful bag of cheetos ;-) Cool technique! Worth checking out the paper; lots of cool new science can be done with this resolution in living systems!

Happy Thanksgiving! We are thankful for the opportunity to talk about how cool genomics is with you all the time. Our family might get annoyed at us for talking about it too much, but that won’t stop us!

Data can be analyzed in endless ways, as this paper reminds us. So while our published paper reports one way to do it, it's crucial to test many many variants of the analysis to see just how robust our conclusions are.

Seeing data on macrophages and metabolism from single cell. It's somewhat buried in the paper and not obvious from the title - but we see different metabolic profiles in macrophages from pro-regenerative vs fibrotic materials in vivo! science.org/doi/10.1126/sc…

If you’re frustrated with healthcare, get this: Health Insurance Elevance Health (formerly Anthem) bamboozled Caltech into overpaying thousands per person for a pricier plan identical to the cheaper one—except for a higher premium and deductible. No added benefits.

Length biases in single-cell RNA sequencing of pre-mRNA. Check out this research by Lior Pachter & Gennady Gorin in Biophysical Reports #CellBio2024 hubs.li/Q02_LC2n0

It's bad that the NIH has pretty much stopped funding young scientists to lead projects

Single-cell RNA sequencing is biased (and more so if you count more molecules). But biases aren't just arbitrary artifacts; they reveal something non-obvious about the technology and chemistry of sequencing, and we can learn a lot by using physical models. Read to find out more!

Wtf is up with deep learning???

This by Sam Rodriques laments the (anticipated) failure of models trained on large datasets to reproduce "real biology discoveries" like those found in Science and Nature. While he sees this as a problem, I COULD NOT DISAGREE MORE. That foundational models don't (and God help us

Some papers seem to be written by authors who did not really know what they wanted to say, and they did not figure it out in the writing process.

Announcing the Elisabeth Bik Science Integrity Fund. retractionwatch.com/2025/02/13/eli…

NIH has stopped considering new grant applications, delaying decisions about how to spend millions of dollars. The freeze occurred because the Trump administration has blocked the NIH… npr.org/sections/shots…

The US mostly funds biomedical research through a large lump payment to the NIH. Panels of scientists and doctors volunteer to decide on the merit of grant individual applications, not unlike a really boring battle royale. All of this is halted. No new science is being funded.