Auguste Genovesio
Computational Bioimaging
and Bioinformatics
This team is part of the Computational Biology Center.
The research project of our team is the study of cellular morphology and dynamics at large scale. We are interested in characterizing the morphological heterogeneity of cellular responses to perturbations. We thus work to identify mechanical or molecular factors of cell morphology, organization and activity in different contexts. In this perspective, on one hand we contribute to generate large data sets of images or gene expression. On the other hand, we interpret these large data sets to produce and validate predictive models. As the scale of the data produced this way constrains us to full automation and quantitative approaches, we develop algorithms and tools for the analysis of large image data. The members of our team bring together a wide range of expertise such as machine and deep learning, computer science, applied mathematics, biophysics and genomic analysis. We apply our approaches to scientific questions we raise such as understanding the action of small compounds with the support of our collaborators from the Curie Institute next door and the pharmaceutical industry. We also develop approaches dedicated to fundamental biology research through a strong interaction with our colleagues at IBENS, Collège de France and ESPCI in various subfields such as functional genomics, developmental biology and neuroscience.
Recent selected publications
(Full list of publications and patents available here )
ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images
N. Bourriez*, I. Bendidi*, E. Cohen*, G. Watkinson, M. Sanchez, G. Bollot, A. Genovesio✉
2024, CVPR doi: 10.48550/arXiv.2311.15264
Weakly supervised cross-modal learning in high-content screening
G. Watkinson*, E. Cohen*, N. Bourriez, I. Bendidi, G. Bollot, A. Genovesio✉
2024, IEEE ISBI doi: 10.48550/arXiv.2311.04678
One Style is All you Need to Generate a Video
S. Manandhar, A. Genovesio✉
2024, IEEE/CVF WACV
Transfer learning for versatile and training free high content screening analyses
M. Corbe, G. Boncompain, F. Perez, E. Del Nery✉ & A. Genovesio✉
2023, Scientific Reports, doi: 10.1038/s41598-023-49554-8
No Free Lunch in Self Supervised Representation Learning
I. Bendidi, A. Bardes, E. Cohen, A. Lamiable, G. Bollot, A. Genovesio✉
2023, NeurIPS, Self-Supervised Learning - Theory and Practice
Revealing invisible cell phenotypes with conditional generative modeling
A. Lamiable*, T. Champetier*, F. Leonardi, E. Cohen, P. Sommer, D. Hardy, N. Argy, A. Massougbodji, E. Del Nery, G. Cottrell, Y.-J. Kwon✉, A. Genovesio✉
2023, Nature communications, doi: 10.1038/s41467-023-42124-6
Cell painting transfer increases screening hit rate
E. Cohen, M. Corbe, C. A. Franco, F. F. Vasconcelos, F. Perez, E. Del Nery, G. Bollot and A. Genovesio✉
2023, Biological Imaging, doi: 10.1017/S2633903X23000077
Evolution is not uniform along coding sequences
R. Bricout, D. Weil, D. Stroebel, A. Genovesio✉, H. Roest Crollius✉
2023, Molecular Biology and Evolution, doi: 10.1093/molbev/msad042
Super-Resolution through StyleGAN Regularized Latent Search
M. Gheizari, and A. Genovesio✉
2022, NeurIPS, Self-Supervised Learning - Theory and Practice
Unpaired Image-to-Image Translation with Limited Data to Reveal Subtle Phenotypes
A. Bourou, K. Daupin, V. Dubreuil, A. De Thonel, V. Lallemand-Mezger, and A. Genovesio✉
2022, NeurIPS, Self-Supervised Learning - Theory and Practice
Comparison of semi-supervised learning methods for High Content Screening quality control
U. Masud∗, E. Cohen∗, I. Bendidi, G. Bollot, and A. Genovesio✉
2022, ECCV, doi: 10.1007/978-3-031-25069-9_26
SAVGAN: Self-Attention based Generation of Tumour on Chip videos
S. Manandhar, I. Veith, M.-C. Parrini, A. Genovesio✉
2022, IEEE ISBI, pp. 1-5, doi: 10.1109/ISBI52829.2022.9761518
Non-convex cell epithelial modeling unveils cellular interactions
E. Laruelle and A. Genovesio✉
2022, IEEE ISBI, pp. 1-5, doi: 10.1109/ISBI52829.2022.9761452
Objective Comparison of High Throughput qPCR Data Analysis Methods
M. Bahin, M. Delagrange, Q. Viautour, J. Pouch, A. Ali Chaouche, B. Ducos✉ and A. Genovesio✉
2021, J Appl Bioinformat Computat Biol S Vol: 10 Issue: 4
Unraveling spatial cellular pattern by computational tissue shuffling
E. Laruelle, N. Spassky✉, A. Genovesio✉
2020, Communications Biology
In vivo large-scale analysis of Drosophila neuronal calcium traces by automated tracking of single somata
F Delestro*, L Scheunemann*, M Pedrezzani, P Tchenio, T Preat✉, A Genovesio✉
2020, Scientific Reports
Active Fluctuations of the Nuclear Envelope Shape the Transcriptional Dynamics in Oocytes
M Almonacid, A Al Jord, S El-Hayek, A Othmani, F Coulpier, S Lemoine, K Miyamoto, R Grosse, C Klein, T Piolot, P Mailly, R Voituriez, A Genovesio✉, M-H Verlhac✉
2019, Developmental Cell
PySpacell: A Python Package for Spatial Analysis of Cell Images.
F Rose, L Rappez, SH Triana, T Alexandrov, A Genovesio✉
2019, Cytometry Part A
ALFA: annotation landscape for aligned reads
M Bahin*, B F Noel*, V Murigneux, C Bernard, L Bastianelli, H Le Hir, A Lebreton✉, A Genovesio✉
2019, BMC Genomics
Monitored eCLIP: high accuracy mapping of RNA-protein interactions
R Hocq*, J Paternina*, Q Alasseur,
2018, Nucleic Acid Research
High‐Throughput Optical Mapping of Replicating DNA
F De Carli*, N Menezes*, W Berrabah, V Barbe,
2018, Small Methods
Smooth 2D manifold extraction from 3D image stack
A Shihavuddin*, S Basu*, E Rexhepaj, F Delestro, N Menezes, SM Sigoillot, E Del Nery, F Selimi, N Spassky,
2017, Nature Communications
Compound Functional Prediction Using Multiple Unrelated Morphological Profiling Assays
F Rose*, S Basu*, E Rexhepaj, A Chauchereau, E Del Nery,
2017, SLAS Technology
Detection and tracking of overlapping cell nuclei for large scale mitosis analyses
Y Li*, F Rose*, F di Pietro, X Morin,
2016, BMC bioinformatics
