Author

Eric Durand

Novartis (Switzerland)

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 2 Approach:machine learning (classical)· 2 Assay:scRNA-seq· 2 Research Focus:batch correction / integration· 1 Research Focus:cell-cell communication· 1 Assay:CITE-seq· 1 Assay:CRISPR screen· 1 Research Focus:deconvolution· 1

Frequent Co-Authors

Antoine de WeckiD· 2 shared Alberto RomagnoniiD· 1 shared Alex J. CornishiD· 1 shared Almudena Espín-PéreziD· 1 shared Anaïs Chossegros· 1 shared Annick WaldtiD· 1 shared Audrey KauffmanniD· 1 shared Baptiste Gross· 1 shared

Activity by Year

2022-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Antoine de WeckORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Deciphering Cellular Ecosystems Driving Tumor Progression and Immune Escape from Spatial Transcriptomics and Single-Cell with COMPOTES

Loïc Herpin, Anaïs Chossegros, Roberta Codato, Josep Monserrat Sanchez, Jean El Khoury, Simon Grouard, Valérie Ducret, Alex J. Cornish, Baptiste Gross, Elodie Pronier, Caroline Hoffmann, Alberto Romagnoni, Eric Durand, Almudena Espín-Pérez, Quentin Bayard

The paper introduces a machine-learning method to identify recurrent cell-cell communication patterns across tumor samples using spatial transcriptomics and single-cell data, revealing key immune and genetic programs in bladder cancer.

bioRxiv · Oct 27, 2025 · PreprintBioinformatics

High-content cellular screen image analysis benchmark study

Mark‐Anthony Bray, Antoine de Weck, Eric Durand, Jian Chen Fang, Daniela Gabriel, Rens Janssens, Ioannis K. Moutsatsos, Stephan Spiegel, Xian Zhang

This study benchmarks deep learning and feature engineering methods for analyzing high-content microscopy images to predict phenotypes and cluster cells without manual annotations.

bioRxiv · May 15, 2022 · PreprintBioinformatics

Probabilistic modeling of ambient noise in single-cell omics data

Caibin Sheng, Rui Lopes, Gang Li, Sven Schuierer, Annick Waldt, Rachel Cuttat, Slavica Dimitrieva, Audrey Kauffmann, Eric Durand, Giorgio G. Galli, Guglielmo Roma, Antoine de Weck

The paper develops scAR, a machine learning model to remove ambient noise from single-cell omics data across multiple technologies, showing high denoising accuracy.

bioRxiv · Jan 14, 2022 · PreprintBioinformatics