A5048541433- Greenhouse Technology and Climate Control
- Plant Water Relations and Carbon Dynamics
- Cancer Immunotherapy and Biomarkers
- Cancer Genomics and Diagnostics
- Leaf Properties and Growth Measurement
- Immunotherapy and Immune Responses
- Light effects on plants
- Radiomics and Machine Learning in Medical Imaging
- Crop Yield and Soil Fertility
- Forest ecology and management
- Cancer Research and Treatments
- Plant nutrient uptake and metabolism
- Monoclonal and Polyclonal Antibodies Research
- Plant Molecular Biology Research
- Lung Cancer Treatments and Mutations
- Horticultural and Viticultural Research
- Remote Sensing in Agriculture
- Cell Image Analysis Techniques
- Irrigation Practices and Water Management
- Myeloproliferative Neoplasms: Diagnosis and Treatment
- Ecology and Vegetation Dynamics Studies
- Machine Learning in Healthcare
- Plant and animal studies
- Acute Myeloid Leukemia Research
- Explainable Artificial Intelligence (XAI)
CentraleSupélec
2017-2025
Université Paris-Saclay
2017-2025
Mathématiques et Informatique pour la Complexité et les Systèmes
2016-2024
Supélec
2022-2023
École Centrale Paris
2008-2021
Laboratoire Mathématiques, Image et Applications
2021
Département Mathématiques et Informatique Appliquées
2016-2020
Laboratoire de Mathématiques
2005-2017
UMR Botanique et Modélisation de l’Architecture des Plantes et des végétations
2005-2013
Institut national de recherche en informatique et en automatique
2007-2012
The availability of patient cohorts with several types omics data opens new perspectives for exploring the disease’s underlying biological processes and developing predictive models. It also comes challenges in computational biology terms integrating high-dimensional heterogeneous a fashion that captures interrelationships between multiple genes their functions. Deep learning methods offer promising multi-omics data. In this paper, we review existing integration strategies based on...
Abstract Metastatic relapse after treatment is the leading cause of cancer mortality, and known resistance mechanisms are missing for most treatments administered to patients. To bridge this gap, we analyze a pan-cancer cohort (META-PRISM) 1,031 refractory metastatic tumors profiled via whole-exome transcriptome sequencing. META-PRISM tumors, particularly prostate, bladder, pancreatic types, displayed transformed genomes compared with primary untreated tumors. Standard-of-care biomarkers...
Tumor heterogeneity represents a major challenge in breast cancer, being associated with disease progression and treatment resistance. Precision medicine has been extensively applied to dissect tumor and, through deeper molecular understanding of the disease, personalize therapeutic strategies. In last years, technological advances have widely improved cancer biology several trials developed translate these new insights into clinical practice, ultimate aim improving patients' outcomes. era...
Abstract Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity such presents analytical challenges demands substantial computing resources. Moreover, proliferation diverse spatial technologies, as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, MACSima PhenoCycler...
Numerical simulation of plant growth has been facing a bottleneck due to the cumbersome computation implied by complex topological structure. In this article, authors present new mathematical model for growth, GreenLab, overcoming these difficulties. GreenLab is based on powerful factorization Fast algorithms are derived deterministic and stochastic trees. The time no longer depends number organs grows at most quadratically with age plant. This finds applications build trees very...
Prediction of phenotypic traits from new genotypes under untested environmental conditions is crucial to build simulations breeding strategies improve target traits. Although the plant response stresses characterized by both architectural and functional plasticity, recent attempts integrate biological knowledge into genetics models have mainly concerned specific physiological processes or crop without architecture, thus may prove limited when studying genotype × environment interactions....
The strong influence of environment and functioning on plant organogenesis has been well documented by botanists but is poorly reproduced in most functional–structural models. In this context, a model interactions proposed between functional mechanisms. GreenLab derived from AMAP models was used. Organogenetic rules give the architecture, which defines an interconnected network organs. considered as collection interacting 'sinks' that compete for allocation photosynthates coming 'sources'. A...
The dynamical system of plant growth GREENLAB was originally developed for individual plants, without explicitly taking into account interplant competition light. Inspired by the models in context forest science mono-specific stands, we propose to adapt method crown projection onto x-y plane GREENLAB, order study effects density on resource acquisition and architectural development.The empirical production equation is extrapolated stands computing exposed photosynthetic foliage area each...
Functional–structural models provide detailed representations of tree growth and their application to forestry seems full prospects. However, owing the complexity architecture, parametric identification such remains a critical issue. We present GreenLab approach for modelling growth. It simulates plasticity in response changes internal level trophic competition, especially topological development cambial The model includes simplified representation based on species-specific description...
Background and AimsPlant population density (PPD) influences plant growth greatly. Functional–structural models such as GREENLAB can be used to simulate development PPD effects on functioning architectural behaviour investigated. This study aims evaluate the ability of predict maize at different PPDs.
Arabidopsis thaliana (L.) Heynh. is used as a model plant in many research projects. However, few models simulate its growth at the whole-plant scale. The present study describes first of integrating organogenesis, morphogenesis and carbon-partitioning processes for aerial subterranean parts throughout development. objective was to analyse competition among sinks they emerge from patterns structural adapted GreenLab estimate organ sink strengths by optimisation against biomass measurements....
This study aimed to characterize the interaction between architecture and source–sink relationships in winter oilseed rape (WOSR): do costs of ramification compromise ratio during seed filling? The GreenLab model is a good candidate address this question because it has been already used describe interactions for other species. However, its adaptation WOSR challenge complexity developmental scheme, especially reproductive phase. Equations were added compute expansion delays ramification,...
Single-cell RNA sequencing (scRNA-seq) technology produces an unprecedented resolution at the level of a unique cell, raising great hopes in medicine. Nevertheless, scRNA-seq data suffer from high variations due to experimental conditions, called batch effects, preventing any aggregated downstream analysis. Adversarial Information Factorization provides robust batch-effect correction method that does not rely on prior knowledge cell types nor specific normalization strategy while being...
To model plasticity of plants in their environment, a new version the functional–structural GREENLAB has been developed with full interactions between architecture and functioning. Emergent properties this were revealed by simulations, particular automatic generation rhythms plant development. Such behaviour can be observed natural phenomena such as appearance fruit (cucumber or capsicum plants, for example) branch formation trees. In model, single variable, source–sink ratio controls...
The development of functional-structural plant models has opened interesting perspectives for a better understanding growth as well potential applications in breeding or decision aid farm management. Parameterization such is however difficult issue due to the complexity involved biological processes and interactions between these processes. estimation parameters from experimental data by inverse methods thus crucial step. This paper presents some results discussions first steps towards...
Mathematical models of plant growth are generally characterized by a large number interacting processes, model parameters and costly experimental data acquisition. Such complexities make parameterization difficult process. Moreover, there is variety that coexist in the literature with an absence benchmarking between different approaches insufficient evaluation. In this context, paper aims at enhancing good modelling practices community increasing design efficiency. It gives overview steps...
The developmental history of blood cancer begins with mutation acquisition and the resulting malignant clone expansion. two most prevalent driver mutations found in myeloproliferative neoplasms— JAK2 V617F CALR m —occur hematopoietic stem cells, which are highly complex to observe vivo. To circumvent this difficulty, we propose a method relying on mathematical modeling statistical inference determine disease initiation dynamics. Our findings suggest that tend occur later life than . results...
Objectives Around 30% of patients with rheumatoid arthritis (RA) do not respond to tumour necrosis factor inhibitors (TNFi). We aimed predict patient response TNFi using machine learning on simple clinical and biological data. Methods used data from the RA ESPOIR cohort train our models. The endpoints were EULAR change in Disease Activity Score (DAS28). compared performances multiple models (linear regression, random forest, XGBoost CatBoost) training set cross-validated them area under...
Around 30% of patients with RA have an inadequate response to MTX. We aimed use routine clinical and biological data build machine learning models predicting EULAR MTX identify simple predictive biomarkers.Models were trained on fulfilling the 2010 ACR/EULAR criteria from ESPOIR Leiden EAC cohorts predict at 9 months (± 6 months). Several compared training set using AUROC. The best model was evaluated external validation cohort (tREACH). model's predictions explained Shapley values extract a...