Lonneke Scheffer presenting the CompAIRR tool for AIRR overlap calculation.#AIRRC6

A new episode of #OnAIRR - the podcast of the AIRR Community is available. GreiffLab 💻🔬💊 and Lindsay Cowell discuss machine learning; identifying and understanding immune signals in AIRR. Subscribe and listen in your favorite app or grab the episode at onairr.airr-community.org

Benchmarking study of baseline methods for immune repertoire classification (using immuneML 🤓) is now published! Congrats, Chakri! 🎉For details, see the thread 🧵

Happy to share my first preprint in the Immunolingo project “Advancing protein language models with linguistics: a roadmap for improved interpretability”, a perspective on adapting LMs for protein sequences with the help of linguistic knowledge. arxiv.org/abs/2207.00982

Happy to announce that compAIRR is now published in Bioinformatics - academic.oup.com/bioinformatics…

Simulated data are for sure not perfect, but are they really subordinate to experimental data for bioinformatics method development and benchmarking? We don't think so! Rather, we see them as complementary, filling different but equally important roles:doi.org/10.1093/bioinf… 1/3

The peer-reviewed version of our antibody-antigen simulation framework Absolut! is now online Nature Computational Science. We now provide additional support for the real-world relevance of Absolut!-data for benchmarking antibody specificity predictions. link: rdcu.be/c1TPr

Our perspective on adapting LMs for protein sequences with the help of linguistic knowledge is now out in Nature Machine Intelligence. Led by Mai Ha, 🧍‍♂️ and Philippe A. Robert. Great collboration with Bartlomiej Swiatczak, Geir Kjetil Sandve and Dag Haug. nature.com/articles/s4225…

Looking for a PhD position in computational immunology? And would you like to experience both living in a winter wonderland (Oslo) and a sunny paradise (San Diego)? Open the thread below 🧵⬇️

New work from my lab on "Weakly supervised identification and generation of adaptive immune receptor sequences associated with immune disease status" led by Andrei Slabodkin and Philippe A. Robert. Great collaboration with Geir Kjetil Sandve and L. M. Sollid. See 🧵 below.

Machine learning on immune receptors/repertoires is an exponentially expanding field, but labeled data to benchmark ML methods are missing. We address this need with our new simulation framework LIgO. biorxiv.org/content/10.110… Led by Maria Chernigovskaya + Milena Pavlović. See 🧵⬇️.

And we're now published in Nature Methods 🥳 Full article here: nature.com/articles/s4159…