Yuta Nagano

Excited to announce that our work on #TCR representation modelling is now out on @CellSystemsCP! You can try our model SCEPTR at sceptr.readthedocs.io 🚀

If anyone uses #Prettier to auto-format #Markdown notes, and wishes there was an auto-bullet list plugin for #NeoVim that has the same formatting conventions, you might like github.com/yutanagano/sma…

Our work on contrastive learning of T cell receptor representations is now out in Cell Systems! Give SCEPTR embeddings a try for your TCR analysis applications: sceptr.readthedocs.io/en/stable/

Quant-immuno people: you should know that @andimscience is hiring! As his former PhD student I can attest his group is a perfect mix of brilliant minds with an open, collaborative culture and you'll have an amazing time. qimmuno.com/openings/

How to learn generalizable rules across complex sequence-function maps? 💡In our new preprint we propose a framework for learning two-point statistics and use it to discover biophysical rules of TCR specificity that generalize to unseen ligands ! ✨ arxiv.org/abs/2412.13722

Using information theory to study the immune receptor code? 👉 check out this short popular summary of our recent @PNASNews paper by first author @jhenderson_sci on @GrowKudos growkudos.com/publications/1…

What are the rules of the immune receptor code? In my talk @KITP_UCSB last Friday I summarised our recent findings, provide a perspective on why ML approaches have so far not achieved breakthrough success, and propose a path forward: online.kitp.ucsb.edu/online/viralim…

Information theory of the T cell receptor sequence-function map -- our paper now out @PNASNews ! 🔥 How informative (in bits) is the α or β chain? When does partial information limit predictions? + insights into synergy, Renyi entropy, optimal compression & more 👉

🚨 Fresh #preprint 🚨 Targeting #DUX4 protein diffusion when designing #FSHD #therapeutics looks to be a great strategy! Read more here: biorxiv.org/content/10.110… Lovely as always working with @FSHresearch, Prof. Pete Zammit, and Dr Christopher Banerji.

@drchristhorpe Thank you so much for the shout-out @drchristhorpe! Very happy to hear it's been helpful.

Of course, I should have mentioned @YutaNotUtah in this tweet. Sorry, Yuto. And a huge thank you for this lovely piece of work

Very happy to share our latest pre-print studying T cell dynamics in multiple myeloma disease evolution 🧵1/10. medrxiv.org/content/10.110…

A huge and sincere thank you to my supervisors @BennyChain @andimscience and co-authors Andrew Pyo @Martina_Milig @jhenderson_sci and Prof. John Shawe-Taylor. It's been great fun and I've learned so much working on this with all of you 🙏💪

We also find that performance on TCR specificity prediction is not currently bottlenecked by model size.

How can we better align protein language model (PLM) pre-training with downstream tasks of interest? For TCR specificity prediction, it turns out that autocontrastive learning is really beneficial! Check out our preprint: arxiv.org/abs/2406.06397

The latest exciting step forward in T cell receptor representation ! @YutaNotUtah outperforms TCR-BERT ! arxiv.org/pdf/2406.06397

How to pre-train protein language model to optimize transfer learning? @YutaNotUtah’s PhD work shows prior PLMs struggle to predict TCR specificty & uses contrastive learning to overcome this limitation. 👉arxiv.org/abs/2406.06397

Really proud and grateful to be part of this amazing work led by @Martina_Milig!

Happy to release our preprint introducing an information theory of T cell specificity! We rank TCR features by their relevance to predicting epitope specificity and bound how accurately T cell specificity can be predicted from partial information. arxiv.org/abs/2404.12565

New preprint: Annie Baker & @BennyChain have led the development of "FUME-TCRseq" a new T-cell receptor seq assay sensitive & robust enough to work on microdissected FFPE samples. Enables profiling of TCR clones & their tissue microenvironment together. biorxiv.org/content/10.110…