Andreas Tiffeau-Mayer
@andimscience
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Quantitative immunology & biological physics. Q-Immuno Lab PI @iit_ucl & @ucl_ipls past: Lewis-Sigler Fellow @Princeton, PhD @ENS_ULM Also @qimmuno.bsky.social
London, UK
Joined February 2014
How can repertoire analysis inform the selection of TCRs for cancer immunotherapy? We've summarised emerging experimental and computational approaches in a review led by @UDemael
mdpi.com
Over the past decade, numerous innovative immunotherapy strategies have transformed the treatment of cancer and improved the survival of patients unresponsive to conventional chemotherapy and...
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RT @PRX_Life: Contrastive learning reveals generalizable rules of T cell receptor co-specificity, highlighting a key role for shape complem….
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Very much looking forward to the Oxford TCR meeting! 😃 Submit your abstract by Friday.
ABSTRACT submission deadline now extended to July 11th! . Register now to join us at this exciting Meeting and Hackathon. . Oxford, Sept 29th - Oct 1st 2025 .#Tcells #AI #Immunology #Oxford #Hackathon
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RT @YEktefaie: 1/ 🧵Can protein language models reason about evolution, not just model it?. We built a 24 M-param PLM, Phyla, that reconstru….
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RT @SaligramaLab: #posting this again if any #postdoc candidates are interested in digging deep into #TCR #Crossreactivity #Tcell functiona….
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RT @mvcowley: A legendary lineup of speakers this week at the Antwerp T cell receptor (TCR) conference! Thanks to the organisers and everyo….
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In our updated preprint, we show that mlTCRdist weights are predictive for pMHCs highly dissimilar (≥ 6 edits) to any seen during training! We are excited about this as such wide extrapolation has so far remained out of reach for sequence-based machine learning approaches. 🎉.
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How to learn rules of TCR-pMHC binding that generalize? We propose that co-specificity rules are likely more universal and can thus be learned from less data.
arxiv.org
The biophysical interactions between the T cell receptor (TCR) and its ligands determine the specificity of the cellular immune response. However, the immense diversity of receptors and ligands...
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Very important work! We've been waiting for someone to do this. It has been clear for some time that a proportion of aggregated TCR-pMHC data might not be reliable. It will be interesting to reassess performance in light of this more robustly validated data.
It's not the models, it's the data!. We show that a substantial proportion of a widely used TCR-pMHC database does not functionally validate, causing underestimation of performance of TCR-pMHC prediction models.
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Two days to go until the 3rd LDN Q-Immuno Day at UCL! We are looking forward to welcome you on Thursday for an incredible line-up of speakers. Full schedule now available here:
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RT @DrGTomlinson: We’re hiring! Exciting #ResearchAssistant opportunity @sheffieldUni using #zebrafish models to study host #immuneresponse….
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RT @Leo1401j: 🚨Postdoc position available in the Fink lab🚨 .At the Pears Building (Div. of Infection & Immunity UCL) . The Fink lab (Dougla….
linkedin.com
Today’s top 3,000+ Postdoctoral Research Assistant jobs in United States. Leverage your professional network, and get hired. New Postdoctoral Research Assistant jobs added daily.
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Two weeks to go for our London Quantitative Immunology day on April 10th! Last chance to submit abstracts for contributed talks is today 👉
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RT @mvcowley: Less than a week left to submit abstracts for the 3rd London Quantitative Immunology Day! Register….
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RT @TheArmita: check out our new manuscript (led by @GMarcoVisani). structure-based machine learning model for TCR-pMHC complexes, predicti….
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Come join us for the 3rd LDN Quantitative Immunology Day on 10th of April @iit_ucl ! All info and free registration 👉
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Exciting to see contrastive learning applied to antibody epitope prediction! 🔥 In our recent @CellSystemsCP article we explored contrastive learning for TCR representations—great to see similar approaches advancing antibody discovery 🚀 Check it out!
Using #antibody sequence alone, can we predict whether two antibodies target overlapping epitopes? A new suite of algorithms developed by @ClintVaccine:. Contrastive Learning Enables Epitope Overlap Predictions for Targeted Antibody Discovery
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RT @hashemkoohy: 🚀 Exciting Announcement: Monthly Webinar Series 🚀.I am delighted to launch our new monthly webinar series:.🔬 Unravelling T….
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RT @chevaliersf: Registration and abstract submission to #ATCR2025 are now open! Join world-renowned experts to discuss everything #Tcellre….
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4. Finally, we used our information theory framework to explain why predictions for some epitopes are easier regardless of model and we introduce a simple technique to provide error bars for comparing model performance which removes the variance resulting from this effect.
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