Serena Chan, Léa Kaufmann, and Carl Munoz present their research at IVADO Digital Futurs 2025

Serena Chan and Léa Kaufmann each presented a poster of their research work, and Carl Munoz was selected for an oral presentation.

The fifth edition of IVADO Digital Futures was a great opportunity for our students to showcase their research to the entire intelligent community (industrial and academic). All presentations had to pay particular attention to scientific popularization and the practical applications of the research work.

AI for RNA-Seq Embeddings in Transcriptomic Profiling

Poster presented by Serena Chan [EN]

Gene embeddings capture co-expression patterns from RNA-seq data, enabling deep neural networks to model gene functions and regulation. This project compares the use of raw expression values with ranking-based embeddings, inspired by natural language processing, across tasks including cell line identification and masked token pretraining. Preliminary results show similar performance for identification, while expression profiles outperform ranked embeddings in masked pretraining, with largest errors at mid-ranked genes. Ongoing work will evaluate downstream tasks and explore hybrid architectures to integrate both representations, aiming to capture complementary transcriptomic features and improve prediction accuracy across tasks throughout precision medicine and drug discovery.

AI for predicting the biological activity of chemical compounds in cancer cells

Poster presented by Léa Kaufmann [EN] Predicting in silico the effect of existing compounds on cancer cells can enable efficient virtual screening of candidate therapeutics. We develop a deep neural network predicting the expression profile of a treated target cell line given four modalities in input: (1) an embedding of the compound’s structure, (2) an embedding of a reference cell line, (3) the expression profile of the reference line treated with the same compound, and (4) an embedding of the target cell line. The embeddings will be derived from open-source foundation models. The prediction validation will be facilitated, as no de novo synthesis will be necessary.

AI for denoising of low-depth RNA-seq data

Poster presented by Carl Munoz [EN] RNA sequencing is a powerful but expensive technology. Reducing the sequencing depth decreases the price, but it also the data quality. As such, we are developing machine-learning-based denoising models to increase data quality while maintaining lower costs. Currently, we developed a neural network that denoises low-depth RNA-seq and recovers most information lost. We are also developing a Bayesian normalizing-flow-based framework as an additional method that better captures uncertainty caused by depth reduction. This research has the potential to change RNA-seq standards by reducing costs for all applications, including data acquisition or costs for personalized medicine.