A5053090823- Cell Image Analysis Techniques
- Advanced Fluorescence Microscopy Techniques
- Bayesian Modeling and Causal Inference
- Single-cell and spatial transcriptomics
- Bioinformatics and Genomic Networks
- Biomedical Text Mining and Ontologies
- Cancer-related molecular mechanisms research
- Hematopoietic Stem Cell Transplantation
- Genomics and Phylogenetic Studies
- Hippo pathway signaling and YAP/TAZ
- Molecular Biology Techniques and Applications
- Mesenchymal stem cell research
- Neural Networks and Applications
- Breast Cancer Treatment Studies
- AI in cancer detection
- Computational Drug Discovery Methods
- Genetics, Bioinformatics, and Biomedical Research
- Cardiovascular Effects of Exercise
- Immune cells in cancer
- AI-based Problem Solving and Planning
- Machine Learning in Healthcare
- Cancer Genomics and Diagnostics
- Cancer Cells and Metastasis
- Data Quality and Management
- Opinion Dynamics and Social Influence
Sorbonne Université
2020-2025
Université Paris Sciences et Lettres
2019-2025
Institut Curie
2019-2025
Centre National de la Recherche Scientifique
2020-2025
Daikin (United States)
2024
Physique des Cellules et Cancers
2019-2020
Institut de Mathématiques de Jussieu-Paris Rive Gauche
2020
Université Paris Cité
2018
We present PyDESeq2, a python implementation of the DESeq2 workflow for differential expression analysis on bulk RNA-seq data. This re-implementation yields similar, but not identical, results: it achieves higher model likelihood, allows speed improvements large datasets, as shown in experiments TCGA data, and can be more easily interfaced with modern python-based data science tools.PyDESeq2 is released an open-source software under MIT license. The source code available GitHub at...
Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data are available. In practice, networks difficult to learn and interpret, limited relatively small datasets. We report a more reliable scalable discovery method (iMIIC), based on general mutual information supremum principle, which greatly improves precision inferred relations while distinguishing genuine causes from putative latent effects. showcase iMIIC synthetic...
Abstract Summary We present PyDESeq2, a python implementation of the DESeq2 workflow for differential expression analysis on bulk RNA-seq data. This achieves better precision, allows speed improvements large datasets, as shown in experiments TCGA data, and can be more easily interfaced with modern python-based data science tools. Availability Implementation PyDESeq2 is released an open-source software under MIT license. The source code available GitHub at https://github.com/owkin/PyDESeq2 ....
Live-cell microscopy routinely provides massive amounts of time-lapse images complex cellular systems under various physiological or therapeutic conditions. However, this wealth data remains difficult to interpret in terms causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers and possibly time-lagged effects from morphodynamic features cell–cell interactions live-cell imaging data. CausalXtract methodology combines network-based information-based...
The precise diagnostics of complex diseases require to integrate a large amount information from heterogeneous clinical and biomedical data, whose direct indirect interdependences are notoriously difficult assess. To this end, we propose an efficient computational approach simultaneously compute assess the significance multivariate between any combination mixed-type (continuous/categorical) variables. method is then used uncover direct, possibly causal relationships data medical records, by...
The cardinal property of bone marrow (BM) stromal cells is their capacity to contribute hematopoietic stem cell (HSC) niches by providing mediators assisting HSC functions. In this study we first contrasted transcriptomes at different developmental stages and then included large number HSC-supportive non-supportive samples. Application a combination algorithms, comprising one identifying reliable paths potential causative relationships in complex systems, revealed gene networks...
Despite unprecedented amount of information now available in medical records, health data remain underexploited due to their heterogeneity and complexity. Simple charts hypothesis-driven statistics can no longer apprehend the content information-rich clinical data. There is, therefore, a clear need for powerful interactive visualization tools enabling practitioners perceive patterns insights gained by state-of-the-art machine learning algorithms. Here, we report an graphical interface use as...
SUMMARY Following infection, hematopoietic stem and progenitor cells (HSPCs) support immunity by increasing the rate of innate immune cell production but metabolic cues that guide this process are unknown. To address question, we developed MetaFate, a method to trace expression state developmental fate single in vivo . Using MetaFate identified gene program enzymes transporters confers differences myeloid differentiation potential subset HSPCs express CD62L. single-cell profiling, confirmed...
Abstract Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact design choices DL approaches on performance learned representation, including model architecture, training methodology and various hyperparameters. To address this problem, we evaluate representation methods using TCGA DepMap pan-cancer datasets, assess their predictive power for survival gene essentiality...
Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact design choices DL approaches on performance learned representation, including model architecture, training methodology and various hyperparameters. To address this problem, we evaluate representation methods using TCGA DepMap pan-cancer datasets assess their predictive power for survival gene essentiality predictions. We...
Live-cell microscopy routinely provides massive amounts of time-lapse images complex cellular systems under various physiological or therapeutic conditions. However, this wealth data remains difficult to interpret in terms causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers and possibly time-lagged effects from morphodynamic features cell–cell interactions live-cell imaging data. CausalXtract methodology combines network-based information-based...
Live-cell microscopy routinely provides massive amount of time-lapse images complex cellular systems under various physiological or therapeutic conditions. However, this wealth data remains difficult to interpret in terms causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers and possibly time-lagged effects from morphodynamic features cell-cell interactions live-cell imaging data. CausalXtract methodology combines network-based information-based...
Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data available. In practice, networks are difficult to learn and interpret, limited relatively small datasets. We report a more reliable scalable discovery method (iMIIC), based on general mutual information supremum principle, which greatly improves precision inferred relations while distinguishing genuine causes from putative latent effects. showcase iMIIC synthetic...
Abstract Live-cell microscopy routinely provides massive amount of time-lapse images complex cellular systems under various physiological or therapeutic conditions. However, this wealth data remains difficult to interpret in terms causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers and possibly time-lagged effects from morphodynamic features cell-cell interactions live-cell imaging data. CausalXtract methodology combines network-based...
Abstract Hippo signaling emerged over the last decade as a major tumor-suppressing pathway. Its dysregulation is generally associated with abnormal expression levels of YAP1, WWTR1 (coding for TAZ protein) and TEAD genes among others. This pathway has been shown to have prognostic impact in several cancer types. In particular, role YAP1/TEAD activity across indications emphasized by recent works, potential implications on treatment options. Therefore, identifying patients deregulated key...
Summary Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD -family genes. Recent works have highlighted role YAP1/TEAD activity in several cancers its potential therapeutic implications. Therefore, identifying patients dysregulated pathway key to enhancing treatment impact. Although recent studies derived RNAseq-based signatures, there remains need for reproducible cost-effective...
Live-cell microscopy routinely provides massive amount of time-lapse images complex cellular systems under various physiological or therapeutic conditions. However, this wealth data remains difficult to interpret in terms causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers and possibly time-lagged effects from morphodynamic features cell-cell interactions live-cell imaging data. CausalXtract methodology combines network-based information-based...
The cardinal property of bone marrow (BM) stromal cells is their capacity to contribute Hematopoietic Stem Cell (HSC) niches by providing mediators assisting HSC functions. In this study we first contrasted transcriptomes at different developmental stages and then included large number HSC-supportive non-supportive samples. Application a combination algorithms enabled identifying gene network characteristic the BM defining niche populations perivascular cells, osteoblasts mesenchymal cells....