Valentina Boeva

Which gene embedding should you use for your model: one learned from single-cell data, protein sequences, or a knowledge graph?
Finding out usually means hours of downloading, ID mapping, and format wrangling. We built Gene Embedding Hub to skip that step.

🧬 67 human gene embedding sets across 10+ modalities
📐Aligned to up to 20,097 protein-coding genes with Ensembl IDs
🤗 Ready to load from Hugging Face: huggingface.co/spaces/BoevaLa…

Congrats on this amazing work Daniel Tabares Lopez and Michael Bohl!👏 🚀
Check out the preprint: biorxiv.org/content/10.648…

Please welcome our preprint on finding spatial cell arrangements linked to patient phenotypes!

Link: biorxiv.org/content/10.648…

GRASS-MIL can use regression, classification, or survival labels as targets and provides several interpretability tools to understand what drives its predictions.

Technically, it combines, in an end-to-end model, graph neural networks with multiple-instance learning: a GNN learns representations of local cellular neighborhoods, while attention-based MIL learns which neighborhoods matter for the patient-level phenotype. Thus, predictions can be decomposed into signed contributions from individual neighborhoods, helping identify and characterize phenotype-associated spatial niches.

We demonstrate the GRASS-MIL's efficiency and utility on simulated data and ovarian and lung cancer spatial datasets, including joint analysis across CosMx, Xenium, and MERFISH for the ovarian cohort. The work was led by Lovro Rabuzin @Rabuzin_L , with contributions and input from Josephine Yates @JoYatesResearch , Mina Xu @MinaXu7 , Martina Haberecker, Raphael Werner, Mayura Meerang, Rong Fan @RongFan8 , and Archibald Enninful.

Meet our lightweight (looped) tabular foundation model for bulk transcriptomics data!
Amazing work by @TheusResearch, Michael Bohl, and David Wissel.
We benchmark our GeneICL model on 80 classification, regression and survival prediction tasks and demonstrate its accuracy and efficiency!

I am very happy to share that I have received tenure at ETH Zürich and am now an Associate Professor in the Department of Computer Science.

So many people have contributed to this milestone, and I want to thank them.

Seva Makeev and Mireille Régnier @MireilleRegnier, who supervised my early work and shaped how I think about science. Emmanuel Barillot and Olivier Delattre, who took a chance on me as a postdoc and let me follow my own questions. Alena Shkumatava @AlenaShkumatava, who helped me take my ATIP-Avenir presentation to a new level to start my lab in Paris. Vassili Soumelis and Michael Lässig, whose advice at a key moment sent me to Zurich. Ganna Panasyuk, for her never-failing love, support, and publication advice. Robert Katzschmann @katzschmann, for eye-opening advice on clean presentations. Simone Schürle, for helping with my ETH tenure talk. Gustavo Alonso, my second mentor, for valuable feedback and advice. And Gunnar Rätsch @gxr, my main ETH mentor, for absolutely incredible support over these seven years.

My group, past and present, and everyone who has passed through the lab. Every result we have published carries your fingerprints, and seeing you build your own careers is the part of this job I am proudest of. My admins, Christine, Natalia, Paulina, Patricia, Tim, and Nicole, for saving me, and the group's funds, more than once. 😉

My colleagues at Institut Curie and Institut Cochin, where the lab was born, and at IML, D-INFK, D-BSSE, D-BIOL, D-HEST, D-MATH, and SDSC, for the honest feedback and the shared projects. My clinical and experimental collaborators at UZH, USZ, Kispi, and Balgrist 💙, for keeping our methods connected to real patients.

My support network of climbing and not-so-much-climbing assistant (and some very cool associate) professors: Patrick, Serina, Virginie, Stefan, Timothée, Quentin, Andreas, Ferdinand, Outi, Victor, Arkadiy, Ender... the list is long. 😉

And my husband, Valentin, who moved countries with me and has been the strongest driving force in my career. ❤️

I feel very lucky to have crossed paths with all of you.

In the next chapter, I hope to bring what we learn from cancer genomes a little closer to the patients who need it. I am looking forward to continuing this journey with you.

Thanks to our amazing team for their participation at #ECCB2026 Comp Biol conference in Geneva! And thanks to @ISBSIB for organizing! It was the best ECCB I have even attended ;-)