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Handling heterogeneous Imaging / signal data for analysing the Neurodevelopmental Trajectories of premature newborns
Medical imaging provides a wide spectrum of modalities giving access to data of various physical natures and dimensions. Each imaging modality provides valuable information for the diagnosis and monitoring of patients. Nevertheless, the linking of these information is made complex by the heterogenei
Etiological diagnosis of cardiac diseases based on echocardiographic images and clinical data
The objective of this project is to develop rigorous and explainable artificial intelligence (AI) models for the prediction of etiological diagnosis of cardiac diseases from heterogeneous inputs. For this purpose, we will create a cohort composed of 1000 patients with 4 distinct pathologies that req
Modelling the effect of apoptosis on epithelial fluidity
Epithelia have a viscoelastic behaviour: they respond as solids over short times and as fluids over large times. This fluidity plays an essential role in morphogenesis and tissue deformation. At the cellular scale, fluidity is achieved by the remodelling of junctions between cells due to their inter
Simulation Based Network Structure Inference Constrained by Observed Spike Trains
Neurophysiologists are nowadays able to record from a large number of extracellular electrodes and to extract, from the raw data, the sequences of action potentials or spikes generated by many neurons. Unfortunately these "many neurons" still represent only a tiny fraction of the neuronal population
Stastistical learning to decipher secretion systems in genomes
Functional annotation is often performed by phylogenomics approaches or machine learning approaches. On the one hand, phylogenomics approaches rely on quantifying sequence similarity and genomic context between the protein of interest and proteins for which functional annotation is available. Partic
Artificial-Intelligence-based Methods for Nanobody Design
ANDES aims at developing and experimentally validating new computational methods for nanobody design, with a large spectrum of applications (therapies, diagnosis…) in order to have efficient technological solutions, ready to react very quickly and to face the emergence of new viruses. Our ambition l
ENERgy driven modelling of tissue architecture emerGENCE and homeorhesis
With the increase in life expectancy in most countries, understanding the mechanisms of tissue decline has become a major public health priority for our society. Decline of tissue functions is correlated with a loss of its architecture. The emergence of this architecture and subsequent maintenance
Aid-decision making platform to PREDICT Abdominal aortic aneurysm outcomes
Cardiovascular diseases are the leading cause of premature death in developed countries. Among them, abdominal aortic aneurysm (AAA) has become a significant health public challenge worldwide, with extremely high rates of mortality in case of rupture. The only curative treatment relies on surgery an
Artificial Intelligence for Virtual Screening and Drug Discovery: Integrated Machine Learning and Molecular Electron Density approach
Virtual screening and docking-scoring methods are essential in preselecting hit molecules for pharmacological targets among millions of compounds. Structure-based methods aim to select the best potential hits for in vitro assays. Docking and scoring remain currently a major challenge in retrieving t
Predictive Ecological Genomics
High-throughput sequencing of genomes of one or many species living in distinct habitats has opened extraordinary perspectives for predicting species responses to climate change. The objective of this research project is to develop statistical methods for predictive ecological genomics (PEG) based o
Multiscale Exploration of RNA poLymorphism for drug desIgN
Non-coding RNA molecules make up for more than 90% of the genome of higher organisms and are known to be key elements in gene expression regulation in viruses. Targeting RNA with drugs would open to a whole new class of possible therapies, now mainly limited to proteins. However, the flexible and dy
Contribution of DNA low complexity regions in genomic regulations
Finding how regulatory DNA sequence operates to control genome expression (i.e. characterizing a DNA cis-regulatory code) is an intense field of research as this information is key to clinically interpret genetic variations and foster genomic medicine. Bioinformatics and machine learning approaches
Causes and consequences of tumor heterogeneity
Cancers are heterogeneous diseases. Variations in tumor composition, gene expression, and response to environmental changes are key factors in cancer progression, while hampering scientific efforts to understand and treat the disease. Current knowledge of cancer biology and corresponding therapeutic
From single cells to populations and back: optogenetic control of microbial communities
One of the core challenges in biology is to understand how behavior at the scale of populations and ecosystems emerges from biochemical processes inside single cells. Central to this problem is that intracellular processes are inherently stochastic and create variability in isogenic microbial popula
Pulmonary Embolism Risk Stratification basEd on Vascular nEtwoRk modElling
Pulmonary embolism (the blockage of a pulmonary artery by a blood clot) is the third cause of cardiovascular death in Europe. Upon diagnosis confirmation, clinicians evaluate the patient prognosis based on risk stratification models. The management of patient, thus its outcome, highly depend on this
Dynamics and control of female germ cell populations: understanding aging through population dynamics models
Female reproductive function is supported by a massive production of specialised germ cells, the oocytes. In women, as in most mammals, the stock of oocytes is established in the peri-natal period and it keeps decreasing all along life, leading to its exhaustion at menopause and to reproductive func
Interactive and Collaborative Learning for Vessel Segmentation
I-VESSEG aims to close the gap hindering the use of 3D vessel segmentation tools to assist clinicians in angiographic clinical routines. The project will build on learning-based techniques and will address their limitations regarding the need for large, fully annotated training sets and their poor g
Computational Design of Intrinsically Disordered Proteins: Application to Flexible Linkers
Up to now, protein structure prediction and design problems have been mostly formulated assuming that proteins fold into a well-defined three-dimensional form. Nevertheless, there is an increasing corpus of work showing that Intrinsically Disordered Proteins/Regions (IDPs/IDRs) perform highly releva
Ventricular Scar Mediated Arrhythmogenesis
The heart is an electrically activated mechanical pump. Its proper functioning is facilitated by interaction between electrical and mechanical systems across spatial scales and physics. Scar is associated with an increased propensity for arrhythmia in the ventricles. Functionally, it alters act
Modeling complex autistic-like behavior in mice and impact of obesity induced during pregnancy
Autism spectrum disorder (ASD) is a frequent neurodevelopmental disease, whose diagnosis is based exclusively on behavioral criteria: i) social interaction deficits and ii) restricted, repetitive and stereotyped patterns of behavior, interests and activities. The etiology of ASD is multifactorial an
Low Energy Optimal Radiofrequency Pulses for MRI
In Nuclear Magnetic Resonance (NMR), radiofrequency (RF) pulses are used to modify the magnetization state of the system. The amplitude and energy of the pulses must comply with both harware limitations (amplifier, coil), and clinical regulation (Specific Absorption Rate). When the desired pulse fai
Intercellular coupling and synchronization between peripheral circadian clocks
InSync is a research project at the interface of mathematics, computation, and biology, with the objective to decipher intercellular synchronization mechanisms responsible for generating robust circadian rhythms in peripheral tissues. Correctly phased oscillations of cellular circadian clocks ensure
Developing in silico avatars of cells to predict and drive cell migration on travelling waves
Cell migration guidance allows cells to reach their target and appropriate location as is the case during embryogenesis. We showed in vitro that curved surfaces guide cell migration locally, with cells always migrating to concave areas and this is independently of gravity. In vivo, the curvature of
Model-based ultrasound characterization of the interface between bone tissue and a dental implant
Implants are widely used in oral surgery. However, there remain risks of failure, which are difficult to anticipate. The main determinant of the implant surgical success is the implant stability at insertion and healing stages. The causes for implant failures, which depend on the biomechanical prope
Deep learning Optical interferometry extracting spatial/temporal correlations of in vivo eye cells
Recently, we studied optical interferometry imaging methods and in particularly the interference patterns originating from the interaction of light waves with an eye. These interference patterns are inherently complex, containing information about the organization of cell structures at sub-micron sc
Revealing the brain's white matter crossing fibers' topology: toward a new generation of tractography algorithms integrating the ground truth neuroanatomy.
In the innovative connectomics field, only diffusion magnetic resonance imaging (dMRI) tractography allows building a complete structural connectome non-invasively. However, dMRI tractography results are still controversial compared to the ground truth white matter (WM) anatomy. DMRI tractography al
Modeling of Irreversible Electroporation for Ventricular Tachycardia
For people with ventricular tachycardia, the electrical wave traveling through the heart is chaotic and this affects the heart's pumping function. One of the major treatments of this arrhythmia is catheter ablation which consists in destroying small areas of heart tissue to isolate or destroy the ca
Network-based biomarker discovery of neurodegenerative diseases using multimodal connectivity
The pathological processes leading to Alzheimer’s (AD) and Parkinson’s (PD) diseases start decades before the onset of the typical clinical symptoms. However, current diagnosis comes quite late in the course of the disease, while evidence underlines the multiple benefits that would be associated wit
Model and reality of radial root water transport
Water uptake by the roots of land plants is a key process for plant survival (and, indirectly, for our survival as well). In the actual context of climate change, water scarcity and water usage conflicts put pressure on farming conditions. A better understanding of water transport from outside of th
Modelling the spatio-temporal dynamics of functional connectivity in resting-state fMRI.
Functional Magnetic Resonance Imaging (fMRI) is the dedicated modality for studying functional brain connectivity (FC). The analysis of fMRI data (3D + time) allows the identification of brain regions whose temporal activity, also called timecourse, is strongly correlated: these regions form what ar
Unbiased massive RNA data-mining for medical applications
High-throughput RNA sequencing (RNA-seq) is a unique tool for the discovery of medical biomarkers and drug targets. However, while nearly one million human RNA-seq libraries are publicly available, this treasure trove of medical information cannot realize its full potential because it is impossible
Microscopic Image filaments' Curvature Extractor using Neural Networks in live cells
Microtubules are semi-flexible and dynamic filaments that play major roles in vivo. During mitosis, they appear highly curved compared to in vitro. It suggests that microtubule bending rigidity may play a role and be regulated. While it is established that microtubule dynamics contribute to cell div
Scalable DNA algorithms
The size of the instances that current experimental settings can solve by DNA assembly is very limitted (typically < 10). This is due to two limiting factors: an important error rate (~1/10.000), and the difficulty to generate enough significantly different strands for the reaction to be selective
New prognostic metastatic phenotypes based on the analysis of whole-body PET images using Artificial Intelligence
Cancer deaths occur in the vast majority of cases in patients with metastatic disease. Currently, although the prognosis depends on the number and type of organs affected, on the degree of invasion and the survival can range from weeks to years, patients with metastatic disease are all grouped into