Artur Szałata

Elon’s power is that he offers a positive vision of the future. This attracts employees, funding, support. There’s a massive techno positive hole and he fills it.

Cellarity Publishes Framework for Discovery of Cell State-Correcting Medicines in Science The Cellarity platform provides critical insights into cellular dysfunction and reveals new biology that we can use to treat disease.

here's the press release too

Active learning framework leveraging transcriptomics identifies modulators of disease phenotypes Phenotypic drug screening remains constrained by the vastness of chemical space and technical challenges scaling experimental workflows. To overcome these barriers, computational methods have been dev...

Active learning with DrugReflector beats SotA in phenotypic hit-rate for virtual screening. Includes a sc perturbation dataset with 10 lines and 104 compounds. Out in @science.org now!
Grateful to Cellarity and @fabiantheis.bsky.social for the opportunity to contribute to this outstanding project!

Three panel thing. In the left panel we use error bars. In the second, we take statistical significance as the biggest number but still have error bars. In LLM science, we just have the biggest number

What if we did a single run and declared victory

Community notes when

Yeah this is my biggest “AGI hype is not real” is that almost no one at these companies behaves like it’s real

My skepticism of LLM-as-scientist stems from how imbalanced the literature is. Median paper is mildly negative result presented as positive, it's unclear how to RLHF on good hypothesis vs. bad hypothesis, etc. We barely know how to teach this skill, how can we RLHF it

For folks considering grad school in ML, my advice is to explore programs that mix ML with a domain interest. ML programs are wildly oversubscribed while a lot of the fun right now is in figuring out what you can do with it

Here0s0Johnny's comment on "Exemplary papers on multi-OMICS integration with solid storytelling" Explore this conversation and more from the bioinformatics community

A must-read before you jump on your first omics project - the top response here www.reddit.com/r/bioinforma...

I think scientists thought people could tell apart the serious science from the bad fluff and ideological work that we all mostly ignore. We were not ready for people to start conflating all of them together

The more rigorous peer review happens in conversations and reading groups after the paper is out with reputational costs for publishing bad work

There are people, in tech (and now in the government!), who will mislead you about what current AI models are capable of. If we don't call them out, they'll drag us all down.

Oops I read my parrot a math textbook and now it keeps squawking out the answer to unseen math competitions

Excited to share that I started my summer at @genentech.bsky.social BRAID Perturbation team in SF with Alex Wu!

It's my first time on the West Coast - If you are around and would like to talk about ML and/or biology, hit me up!

Looking fwd to the AI x Bio Unconference tomorrow 🚀

Analyzing your single-cell data by mapping to a reference atlas? Then how do you know the mapping actually worked, and you’re not analyzing mapping-induced artifacts? We developed mapQC, a mapping evaluation tool www.biorxiv.org/content/10.1... from the ‪@fabiantheis lab. Let’s dive in🧵

From cell lines to full embryos, drug treatments to genetic perturbations, neuron engineering to virtual organoid screens — odds are there’s something in it for you!

Built on flow matching, CellFlow can help guide your next phenotypic screen: biorxiv.org/content/10.1101/2025.04.11.648220v1

At this point, the art of detecting which claims and publications are overhyped is a core research skill.

Just a gentle reminder that deceptive hyping in scientific publications (which includes preprints) is actually antithetical to the core mission of the scientific process. We can stay grounded, truthful, humble while being ambitious. Revealing caveats, pitfalls & limitations speeds up progress.

Our paper benchmarking feature selection for scRNA-seq integration and reference usage is out now www.nature.com/articles/s41...!

Keep reading for more about how we did the study and what we found out 🧵 👇

1/16

Exciting dataset!

If you're looking for a complimentary scRNA-seq drug perturbations in healthy/primary tissue (PBMCs), check out our dataset with ~36% # drugs of Tahoe. proceedings.neurips.cc/paper_files/...

The fact that we seem to be marching straight towards another cold war, where AI is the defining technology, is hard to emotionally accept and even harder to deeply accept how hard some of the next few years can become.

GitHub - huggingface/open-r1: Fully open reproduction of DeepSeek-R1 Fully open reproduction of DeepSeek-R1. Contribute to huggingface/open-r1 development by creating an account on GitHub.

Last moments of closed-source AI 🪦 :
Hugging Face is openly reproducing the pipeline of 🐳 DeepSeek-R1. Open data, open training. open models, open collaboration.

🫵 Let's go!
github.com/huggingface/...

It was the first publication I had the chance to work on, back as a MSc student. I was lucky to be mentored by Slavica Dimitrieva, who led the project, and to have worked on it with Eric Durand. Both inspired me to continue on the bio-ML trajectory 🚀

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning A proposed deep learning method helps to bridge the gap between preclinical cancer models and clinical tumors.

Trying to identify preclinical models that resemble clinical tumors you work on? Check out our MOBER, now out in @science.org Advances! www.science.org/doi/10.1126/... . There's also a web app to explore the results mober.pythonanywhere.com

When a paper is published, any data must be easily and completely accessible, or the publication is a sham and should be retracted editorially.

The speaker was describing some situation of student misconduct and without any reason or justification mentioned the nationality of the student.

1/7 Planning to build a single-cell atlas? Or wondering how atlases can be useful to your research? Read our guide on single-cell atlases www.nature.com/articles/s41... published in Nature Methods, by @lisasikkema.bsky.social, @khrovatin.bsky.social, Malte Luecken, @fabiantheis.bsky.social et al.

1/🚀 Excited to share RegVelo, our new cell model combining RNA velocity with gene regulatory network (GRN) dynamics to model cellular changes and predict in silico perturbations. Here's how it works and why it matters! 🧵👇
biorxiv.org/content/10.1101/2024.12.11.627935v1

4️⃣ “A benchmark for prediction of transcriptomic responses to chemical perturbations across cell types”

@chatgtp.bsky.social

neurips.cc/virtual/2024...

What are the most interesting datasets and benchmark-related work for ML in drug discovery at NeurIPS?

We’ll be at the conference doing short interviews with researchers and handing out some Polaris merch!

Here’s who we have on the shortlist. 🧵