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(1/n) A fresh preprint in which we present a novel approach to T-Cell Receptor Beta chain (TCRB) repertoire encoding using lossless string compression. biorxiv.org/content/10.110…

Efficient evolution of human antibodies from general protein language models go.nature.com/40PRYMN

Excited to present the Hoehn Lab Geisel School of Medicine at Dartmouth website! Opening doors this September. Already have a postdoc position open - please get in touch if interested in B cell phylogenetics with single cell data. More to come soon. sites.dartmouth.edu/hoehn/

1/5 🎉 Thrilled to announce our paper on a novel approach to T-cell receptor beta chain (TCRB) repertoire encoding using lossless string compression has been published in Bioinformatics! This work is pivotal for our daily work and analysis of TCRB's #Bioinformatics #Immunology

biorxiv.org/content/10.110…. A good reminder that predictions of TCRs recognizing 'unseen' epitopes (i.e., epitopes without any known TCR) remains an unsolved challenge.

(1/8)🎉A fresh preprint in which we present LIgO — a powerful tool to simulate adaptive immune receptor (AIR) and repertoire (AIRR) data for the development and benchmarking of AIRR-based ML 🧵⬇️ biorxiv.org/content/10.110…

The IMMREP23 Kaggle #TCR-#epitope specificity #MachineLearning #prediction challenge is live now! Spread the word! airr_community @iRecRepPapers kaggle.com/competitions/t…

(1/7) Curious about what a blood sample can reveal about humoral immunity? Our latest work combined sequencing methods and mass spectrometry to explore BCR and antibody repertoires and assess their synergistic potential. biorxiv.org/content/10.110…

We have trained ESM3 and we're excited to introduce EvolutionaryScale. ESM3 is a generative language model for programming biology. In experiments, we found ESM3 can simulate 500M years of evolution to generate new fluorescent proteins. Read more: evolutionaryscale.ai/blog/esm3-rele…

biorxiv.org/content/10.110…

The race to first-in-human is accelerating globally. The obvious response is to scale up: more automation, more cells, more sequencing, more capital. This will work until it doesn't. At @JURA_Bio, we've been building beyond the obvious.