Thanks to all the coauthors, Eszter Varga, Daniel Dimitrov,
@juliosaezrod.bsky.social, László Hunyady and especially to first author Szilvia Barsi who led this project from start to finish! 7/7

RIDDEN is available as an easy to use command line tool at github.com/basvaat/RIDD.... 6/n

Using an IO dataset of #nivolumab-treated renal cancer patients, we found that:
- #PD1 or #PDL1 expression didn't predict survival
- PD1 activity (from RIDDEN) did associate with survival.
Showing that receptor activity could be a more effective biomarker than expression. 5/n

RIDDEN’s receptor-specific signatures align with regulons of transcription factors downstream of receptors. 4/n

We benchmarked RIDDEN on various receptor perturbation datasets, including the recent in vivo Immune Dictionary data www.nature.com/articles/s41..., and found good predictive performance. 3/n

We used prior knowledge from omnipathdb.org & perturbation data from clue.io to build models for receptor activity inference. Instead of focusing on receptor expression, we predicted activity from the expression of receptor-regulated genes. 2/n

Thanks all the co-authors, Gema Sanz, @kristofszalay.com and especially Gerold Csendes, who led this project! 5/5

Several popular Perturb-seq based benchmark datasets lack heterogeneity, making it difficult to distinguish between strong and weak models. 4/5

Gene embeddings from foundation models align more closely with gene regulatory networks than with signaling networks, which may underlie their weaker performance in perturbation tasks. 3/5

Even the most trivial baseline (mean of train samples) outperformed recent foundation models, while basic ML models using biologically meaningful features won by a large margin. 2/5