
Assistant professor, Wash U in St. Louis, @wusm_pathology
Saint Louis, MO
Joined June 2010
- Tweets5.6K
- Following374
- Followers809
- Likes4.6K
Pinned Tweet
I'm happy to present our new web-tool CORESH alserglab.wustl.edu/coresh/ for searching public gene expression datasets using gene signature as a query, developed by @vd_sukhov, which was just published in @NAR_open doi.org/10.1093/nar/gkaf372 1/n
Alexey Sergushichev retweeted
Смотрите, какая актуальная игра по нашим-то временам!
(На самом деле там не напрямую про цензуру, а просто хорошее интеллектуальное упражнение.)
Alexey Sergushichev retweeted
Out today in Immunity! Our pan-atlas of human immune aging: 12.4 million PBMCs from 2,609 people across 8 global cohorts (including new 188 people cohort from UK).
cell.com/immunity/fulltext/S…
We map shared aging patterns onto 59 key immune populations and link the balance of GZMK+ and GZMB+ CD8 effector memory T cells to health trajectories; show differences in RTEs and Th2s with sex etc; ethnicity associated features.
artyomovlab.wustl.edu/human-…
How it came to be:
in 2023 we published first hierarchically annotatied atlas of human immune aging in PBMC - 166 people, 55 populations, key changes revealed - CD8 naives, KLRC2/NKG2C+ CD8 memory going down, GZMK+ CD8 T cells and Th2 and Tc2 type 2 memory cells going up! (Terekhova et al, Immunity 2023)
in 2024 we added major phenotypes within naive cells - SOX4+ RTE cells and their CD38 marker going down. (Bohacova et al, Immunity 2024)
In 2025-26 more atlases published with their own annotations making impossible to leverage power of data..
Now we provide unique in its power and consistency resource - 2600 people across ages, sexes, races, continents etc. All annotated in sync and analyzed for what aging looks like globally!
Alexey Sergushichev retweeted
FYI I wrapped a lot of immuno-informatics predictors so you don't have to:
openvax.github.io/mhctools/
(eg NetMHCpan, MHCflurry, Pepsickle, DeepTAP, ERAMER, BigMHC, PRIME, &c)
Alexey Sergushichev retweeted
New preprint led by @dmitrij22981 & @ivan_molotkov_ :
Genetic data improves prediction beyond clinical history in 96/150 diseases in @uk_biobank & @FinnGen_FI and is most valuable for people at low clinical risk
Preprint: shorturl.at/AEYQw
Models: shorturl.at/Dfla0🧵
Alexey Sergushichev retweeted
🧬 EPIC is open: the Eukaryotic Promoter and transcription Initiation prediction Challenge.
The goal: predict where, and how strongly, RNA Pol II starts transcribing, from nothing but DNA sequence. At single-nucleotide, strand-specific resolution.
epic.autosome.org
🧵 1/7
Alexey Sergushichev retweeted
What if fucking web developers started using input html tags with proper type= at least for email/password fields so browser autocompete didn't require AI 😭
what if copy/paste was smart?
powered by @typesafeai jev
it feels like every computer interaction will get rewritten
Alexey Sergushichev retweeted
Knuth is a legend, but if P = NP, then we already know explicit polynomial time algorithms that can solve NP-complete problems! Here is a simple (partial) example: 1/7
Alexey Sergushichev retweeted
meet @mostik_ai!
what happens when you put 12 PhDs in one room for four months? first place on the ARC-AGI leaderboard, which I can't say much about while the competition is still running. and this, which I can.
everyone's arguing about whether open models will catch up to frontier models. we think it's the wrong question. here's the one we pose: why does a frontier model have to generate your answer at all, when the only thing you need from it is the reasoning?
we do this by enabling models to communicate in latent space. through our protocol, hidden states pass straight from a frontier model into a small one running on your infrastructure -- no text between them, and neither model is fine-tuned. two models from different families, sharing reasoning, both left untouched.
how do we know it works? we tested it on a setup where a 753B model reads the problem, and a 4B edge-class model writes the answer. with this approach, we get results 80% as accurate as the frontier model, but at 20x faster performance.
we're committed to preventing frontier model lock-in and are already partnering with inference providers to accelerate open-weight adoption. we've done this between 15 of us, in four months, 12 PhDs and a Fields medalist, backed by @generalcatalyst
WIRED has the first external account of the company and the work: wired.com/story/russian-star…
full writeup, the setup, and all the numbers: mostik.ai/read-more
New application note from the lab: "GAMclust: identification of regulated metabolic modules in bulk, single cell and spatial gene expression data" 1/5
But it can work with spatial Vizium data too: as a case study we analyzed a glioma dataset: alserglab.wustl.edu/GAMclust… There, one of the modules is glycolysis, which localizes nicely to the tumor core 4/5
Link for the paper doi.org/10.1093/bioinformati… Source code is at github.com/alserglab/GAMclus… Documentation is at alserglab.wustl.edu/GAMclust… 5/5
I released new 0.4 version of mascarade package with a much improved label placement algorithm. It's a pretty neat algorithm, but it took a while to get there. 1/8
All of this I did with Claude. It definitely helped trying out new ideas (and I could do it remotely from my phone at random places where I get new ideas), but I wasn't able to make it solve this on its own. And it took a LOT of my guidance to make the code look reasonable. 7/8
Thanks @shitov_happens for the challenging UMAP to test on! Get the latest mascarade version at GitHub: github.com/alserglab/mascara… 8/8



















