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1. 3D-based RNA function prediction tools in rnaglib

Author

Oliver, Carlos, Mallet, Vincent, and Waldispühl, Jérôme

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract

Understanding the connection between complex structural features of RNA and biological function is a fundamental challenge in evolutionary studies and in RNA design. However, building datasets of RNA 3D structures and making appropriate modeling choices remains time-consuming and lacks standardization. In this chapter, we describe the use of rnaglib, to train supervised and unsupervised machine learning-based function prediction models on datasets of RNA 3D structures.

Published
2024

3. Ketamine restriction correlates with reduced cholestatic liver injury and improved outcomes in critically ill patients with burn injury.

Author

De Tymowski, Christian, Dépret, François, Dudoignon, Emmanuel, Moreno, Nabila, Zagdanski, Anne-Marie, Hodjat, Kyann, Deniau, Benjamin, Mebazaa, Alexandre, Legrand, Matthieu, and Mallet, Vincent

Subjects
Burn injury, Cholestatic liver injury, Drug toxicity, Drug-induced liver injury, Ketamine, Mortality
Abstract

BACKGROUND & AIMS: Ketamine-associated cholestatic liver injury is reported in patients with severe burn injury, but its association with patient outcome is unclear. We investigated the relationship between ketamine exposure, cholestatic liver injury, and outcome of critically ill patients with burn injury. METHODS: In a retrospective study, patients with severe burn injury were analysed across two periods: unrestricted ketamine prescription (ketamine-liberal) and capped ketamine dosage (ketamine-restricted). The primary endpoint was cholestatic liver injury, and the secondary endpoint was 3-month mortality. Binary logistic regression models and the revised electronic causality assessment method were used to measure the strength of associations and causality assessment, respectively. RESULTS: Of 279 patients (median age 51 [IQR 31-67] years; 63.1% men; burned surface area 28.5%, IQR 20-45%), 155 (56%) were in the ketamine-liberal group, and 124 (44%) were in the ketamine-restricted group, with comparable clinical characteristics, except for ketamine exposure (median doses 265.0 [IQR 0-8,021] mg and 20 [IQR 0-105] mg, respectively; p

Published
2024

4. AtomSurf : Surface Representation for Learning on Protein Structures

Author

Mallet, Vincent, Attaiki, Souhaib, and Ovsjanikov, Maks

Subjects
Computer Science - Machine Learning, Quantitative Biology - Biomolecules
Abstract

An essential aspect of learning from protein structures is the choice of their representation as a geometric object (be it a grid, graph, or surface), which conditions the associated learning method. The performance of a given approach will then depend on both the representation and its corresponding learning model. In this paper, we investigate representing proteins as $\textit{surfaces embedded in 3D}$ and evaluate this representation within an established benchmark: atom3d. Our first finding is that despite promising results, state-of-the-art surface-based learning approaches alone are not competitive with other modalities on this benchmark. Building on this, we introduce a novel synergistic approach that incorporates graph and surface-based approaches within a single learnable architecture. We show that using this combination, which inherits the strengths of the two representations, we obtain state-of-the-art results across $\textit{all tested tasks}$, on the atom3d benchmark, as well as on binding pocket classification. Our code and data can be found online: https://github.com/Vincentx15/atom2D., Comment: 10 pages

Published
2023

5. Approximate Network Motif Mining Via Graph Learning

Author

Oliver, Carlos, Chen, Dexiong, Mallet, Vincent, Philippopoulos, Pericles, and Borgwardt, Karsten

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets. However, the high computational complexity of identifying motif sets in arbitrary datasets (motif mining) has limited their use in many real-world datasets. By automatically leveraging statistical properties of datasets, machine learning approaches have shown promise in several tasks with combinatorial complexity and are therefore a promising candidate for network motif mining. In this work we seek to facilitate the development of machine learning approaches aimed at motif mining. We propose a formulation of the motif mining problem as a node labelling task. In addition, we build benchmark datasets and evaluation metrics which test the ability of models to capture different aspects of motif discovery such as motif number, size, topology, and scarcity. Next, we propose MotiFiesta, a first attempt at solving this problem in a fully differentiable manner with promising results on challenging baselines. Finally, we demonstrate through MotiFiesta that this learning setting can be applied simultaneously to general-purpose data mining and interpretable feature extraction for graph classification tasks.

Published
2022

6. Edge-similarity-aware Graph Neural Networks

Author

Mallet, Vincent, Oliver, Carlos G., and Hamilton, William L.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Graph are a ubiquitous data representation, as they represent a flexible and compact representation. For instance, the 3D structure of RNA can be efficiently represented as $\textit{2.5D graphs}$, graphs whose nodes are nucleotides and edges represent chemical interactions. In this setting, we have biological evidence of the similarity between the edge types, as some chemical interactions are more similar than others. Machine learning on graphs have recently experienced a breakthrough with the introduction of Graph Neural Networks. This algorithm can be framed as a message passing algorithm between graph nodes over graph edges. These messages can depend on the edge type they are transmitted through, but no method currently constrains how a message is altered when the edge type changes. Motivated by the RNA use case, in this project we introduce a graph neural network layer which can leverage prior information about similarities between edges. We show that despite the theoretical appeal of including this similarity prior, the empirical performance is not enhanced on the tasks and datasets we include here.

Published
2021

7. RNAglib: A Python Package for RNA 2.5D Graphs

Author

Mallet, Vincent, Oliver, Carlos, Broadbent, Jonathan, Hamilton, William L., and Waldispühl, Jérôme

Subjects
Quantitative Biology - Quantitative Methods, Quantitative Biology - Molecular Networks
Abstract

RNA 3D architectures are stabilized by sophisticated networks of (non-canonical) base pair interactions, which can be conveniently encoded as multi-relational graphs and efficiently exploited by graph theoretical approaches and recent progresses in machine learning techniques. RNAglib is a library that eases the use of this representation, by providing clean data, methods to load it in machine learning pipelines and graph-based deep learning models suited for this representation. RNAglib also offers other utilities to model RNA with 2.5D graphs, such as drawing tools, comparison functions or baseline performances on RNA applications. The method and data is distributed as a fully documented pip package. Availability: https://rnaglib.cs.mcgill.ca

Published
2021
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10. Implementation of a liver health check in people with type 2 diabetes

Author

Abeysekera, Kushala W M, Valenti, Luca, Younossi, Zobair, Dillon, John F, Allen, Alina M, Noureddin, Mazen, Rinella, Mary E, Tacke, Frank, Francque, Sven, Ginès, Pere, Thiele, Maja, Newsome, Philip N, Guha, Indra Neil, Eslam, Mohammed, Schattenberg, Jörn M, Alqahtani, Saleh A, Arrese, Marco, Berzigotti, Annalisa, Holleboom, Adriaan G, Caussy, Cyrielle, Cusi, Kenneth, Roden, Michael, Hagström, Hannes, Wong, Vincent Wai-Sun, Mallet, Vincent, Castera, Laurent, Lazarus, Jeffrey V, and Tsochatzis, Emmanuel A

Published
2024
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11. VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network Motifs

Author

Oliver, Carlos, Mallet, Vincent, Philippopoulos, Pericles, Hamilton, William L., and Waldispuhl, Jerome

Subjects
Quantitative Biology - Molecular Networks, Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract

RNA 3D motifs are recurrent substructures, modelled as networks of base pair interactions, which are crucial for understanding structure-function relationships. The task of automatically identifying such motifs is computationally hard, and remains a key challenge in the field of RNA structural biology and network analysis. State of the art methods solve special cases of the motif problem by constraining the structural variability in occurrences of a motif, and narrowing the substructure search space. Here, we relax these constraints by posing the motif finding problem as a graph representation learning and clustering task. This framing takes advantage of the continuous nature of graph representations to model the flexibility and variability of RNA motifs in an efficient manner. We propose a set of node similarity functions, clustering methods, and motif construction algorithms to recover flexible RNA motifs. Our tool, VeRNAl can be easily customized by users to desired levels of motif flexibility, abundance and size. We show that VeRNAl is able to retrieve and expand known classes of motifs, as well as to propose novel motifs.

Published
2020
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12. Burden of grade 3 or 4 liver injury associated with immune checkpoint inhibitors

Author

Alexandre, Jérôme, Arrondeau, Jennifer, Boudou-Rouquette, Pascaline, De Percin, Sixtine, Kramkimel, Nora, Huillard, Olivier, Chapron, Jeanne, Deau-Fischer, Benedicte, Brandely-Piat, Marie-Laure, Damotte, Diane, Lupo, Audrey, Alifano, Marco, Corouge, Marion, Hollande, Clémence, Fontaine, Hélène, Mehiri, Lorianne Lair, Pichard, Anaïs Vallet, Tilleul, Patrick, Parlati, Lucia, Sakka, Mehdi, Retbi, Aurelia, Bouam, Samir, Hassani, Lamia, Meritet, Jean-François, Rufat, Pierre, Bonnefont-Rousselot, Dominique, Batista, Rui, Terris, Benoit, Bellanger, Agnès, Thabut, Dominique, Vozy, Aurore, Spano, Jean-Philippe, Coriat, Romain, Goldwasser, François, Aractingi, Selim, Sogni, Philippe, Pol, Stanislas, and Mallet, Vincent

Published
2023
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13. Leveraging binding-site structure for drug discovery with point-cloud methods

Author

Mallet, Vincent, Oliver, Carlos G., Moitessier, Nicolas, and Waldispuhl, Jerome

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

Computational drug discovery strategies can be broadly placed in two categories: ligand-based methods which identify novel molecules by similarity with known ligands, and structure-based methods which predict molecules with high-affinity to a given 3D structure (e.g. a protein). However, ligand-based methods do not leverage information about the binding site, and structure-based approaches rely on the knowledge of a finite set of ligands binding the target. In this work, we introduce TarLig, a novel approach that aims to bridge the gap between ligand and structure-based approaches. We use the 3D structure of the binding site as input to a model which predicts the ligand preferences of the binding site. The resulting predictions could then offer promising seeds and constraints in the chemical space search, based on the binding site structure. TarLig outperforms standard models by introducing a data-alignment and augmentation technique. The recent popularity of Volumetric 3DCNN pipelines in structural bioinformatics suggests that this extra step could help a wide range of methods to improve their results with minimal modifications.

Published
2019

14. Association between hydroxocobalamin administration and acute kidney injury after smoke inhalation: a multicenter retrospective study.

Author

Dépret, François, Hoffmann, Clément, Daoud, Laura, Thieffry, Camille, Monplaisir, Laure, Creveaux, Jules, Annane, Djillali, Parmentier, Erika, Mathieu, Daniel, Wiramus, Sandrine, Demeure DIt Latte, Dominique, Kpodji, Aubin, Textoris, Julien, Robin, Florian, Klouche, Kada, Pontis, Emmanuel, Schnell, Guillaume, Barbier, François, Constantin, Jean-Michel, Clavier, Thomas, du Cheyron, Damien, Terzi, Nicolas, Sauneuf, Bertrand, Guerot, Emmanuel, Lafon, Thomas, Herbland, Alexandre, Megarbane, Bruno, Leclerc, Thomas, Mallet, Vincent, Pirracchio, Romain, and Legrand, Matthieu

Subjects
Humans, Smoke Inhalation Injury, Hydroxocobalamin, Hematinics, Odds Ratio, Retrospective Studies, Smoke, Adult, Middle Aged, Intensive Care Units, France, Female, Male, Acute Kidney Injury, Acute kidney injury, Burn, Intensive care unit, Mortality, Smoke inhalation, Medical and Health Sciences, Emergency & Critical Care Medicine
Abstract

BackgroundThe use of hydroxocobalamin has long been advocated for treating suspected cyanide poisoning after smoke inhalation. Intravenous hydroxocobalamin has however been shown to cause oxalate nephropathy in a single-center study. The impact of hydroxocobalamin on the risk of acute kidney injury (AKI) and survival after smoke inhalation in a multicenter setting remains unexplored.MethodsWe conducted a multicenter retrospective study in 21 intensive care units (ICUs) in France. We included patients admitted to an ICU for smoke inhalation between January 2011 and December 2017. We excluded patients discharged at home alive within 24 h of admission. We assessed the risk of AKI (primary endpoint), severe AKI, major adverse kidney (MAKE) events, and survival (secondary endpoints) after administration of hydroxocobalamin using logistic regression models.ResultsAmong 854 patients screened, 739 patients were included. Three hundred six and 386 (55.2%) patients received hydroxocobalamin. Mortality in ICU was 32.9% (n = 243). Two hundred eighty-eight (39%) patients developed AKI, including 186 (25.2%) who developed severe AKI during the first week. Patients who received hydroxocobalamin were more severe and had higher mortality (38.1% vs 27.2%, p = 0.0022). The adjusted odds ratio (95% confidence interval) of AKI after intravenous hydroxocobalamin was 1.597 (1.055, 2.419) and 1.772 (1.137, 2.762) for severe AKI; intravenous hydroxocobalamin was not associated with survival or MAKE with an adjusted odds ratio (95% confidence interval) of 1.114 (0.691, 1.797) and 0.784 (0.456, 1.349) respectively.ConclusionHydroxocobalamin was associated with an increased risk of AKI and severe AKI but was not associated with survival after smoke inhalation.Trial registrationClinicalTrials.gov, NCT03558646.

Published
2019

15. FRI-215-YI MASL-B registry: results from a european cohort of patients with chronic hepatitis B and metabolic dysfunction-associated steatotic liver disease

Author

Forlano, Roberta, primary, Martinez-Gili, Laura, additional, Lacey, Madeleine, additional, Sigon, Giordano, additional, Mullish, Benjamin H., additional, Mallet, Vincent, additional, Parlati, Lucia, additional, Richardson, Paul, additional, Forde, Niamh, additional, Serviddio, Gaetano, additional, Villani, Rosanna, additional, Lens, Sabela, additional, Buti, Maria, additional, Viganò, Mauro, additional, Loglio, Alessandro, additional, Lampertico, Pietro, additional, D'Ambrosio, Roberta, additional, Monico, Sara, additional, Maffi, Gabriele, additional, Lombardi, Rosa, additional, Fracanzani, Anna Ludovica, additional, De Luca, Chiara, additional, Ingiliz, Patrick, additional, Mangia, Alessandra, additional, Leserre, Federica, additional, Piazzolla, Valeria, additional, Russo, Francesco Paolo, additional, Raimondo, Giovanni, additional, Papatheodoridis, George, additional, Papatheodoridi, Margarita, additional, Samonakis, Dimitrios N., additional, Barnes, Eleanor, additional, O'Donnell, Denise, additional, O'Donoghue, Jennifer, additional, Abeles, R. Daniel, additional, Pugliese, Nicola, additional, Aghemo, Alessio, additional, Lemoine, Maud, additional, Thursz, Mark R., additional, Brown, Ashley, additional, and Manousou, Pinelopi, additional

Published
2024
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17. OS-084-YI Hepatopulmonary syndrome in patients with porto-sinusoidal vascular disease: prevalence, characteristics, risk factors and outcome

Author

Spaes, Ylang, primary, Sidali, Sabrina, additional, El Husseini, Kinan, additional, Goria, Odile, additional, Mallet, Vincent, additional, Poujol-Robert, Armelle, additional, Gervais, Anne, additional, Lannes, Adrien, additional, Thabut, Dominique, additional, Nousbaum, Jean-Baptiste, additional, Ollivier-Hourmand, Isabelle, additional, Costentin, Charlotte, additional, Heurgue-berlot, Alexandra, additional, Houssel-Debry, Pauline, additional, Hillaire, Sophie, additional, Ganne-Carrié, Nathalie, additional, Drilhon, Nicolas, additional, Valainathan, Shantha, additional, Plessier, Aurélie, additional, Durand, Francois, additional, Raevens, Sarah, additional, Cachier, Agnes, additional, Elkrief, Laure, additional, and Rautou, Pierre-Emmanuel, additional

Published
2024
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18. AlignScape, displaying sequence similarity using self-organizing maps

Author

Barcelona Supercomputing Center, Filella Merce, Isaac, Mallet, Vincent, Durand, Enric, Nilges, Michael, Bouvier, Guillaume, Pellarin, Riccardo, Barcelona Supercomputing Center, Filella Merce, Isaac, Mallet, Vincent, Durand, Enric, Nilges, Michael, Bouvier, Guillaume, and Pellarin, Riccardo

Abstract

The current richness of sequence data needs efficient methodologies to display and analyze the complexity of the information in a compact and readable manner. Traditionally, phylogenetic trees and sequence similarity networks have been used to display and analyze sequences of protein families. These methods aim to shed light on key computational biology problems such as sequence classification and functional inference. Here, we present a new methodology, AlignScape, based on self-organizing maps. AlignScape is applied to three large families of proteins: the kinases and GPCRs from human, and bacterial T6SS proteins. AlignScape provides a map of the similarity landscape and a tree representation of multiple sequence alignments These representations are useful to display, cluster, and classify sequences as well as identify functional trends. The efficient GPU implementation of AlignScape allows the analysis of large MSAs in a few minutes. Furthermore, we show how the AlignScape analysis of proteins belonging to the T6SS complex can be used to predict coevolving partners., The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The work was funded by the ANR T6-PLATFORM, ANR CAMELEON, ANR-17-CE11-0039, Inception program ANR-16-CONV-0005, and the Department of Structural Biology and Chemistry of Institut Pasteur., Peer Reviewed, Postprint (published version)

Published
2024

21. Mental disorders, COVID-19-related life-saving measures and mortality in France: A nationwide cohort study

Author

Schwarzinger, Michaël, Luchini, Stéphane, Teschl, Miriam, Alla, François, Mallet, Vincent, and Rehm, Jürgen

Subjects
Biological sciences
Abstract

Background Meta-analyses have shown that preexisting mental disorders may increase serious Coronavirus Disease 2019 (COVID-19) outcomes, especially mortality. However, most studies were conducted during the first months of the pandemic, were inconclusive for several categories of mental disorders, and not fully controlled for potential confounders. Our study objectives were to assess independent associations between various categories of mental disorders and COVID-19-related mortality in a nationwide sample of COVID-19 inpatients discharged over 18 months and the potential role of salvage therapy triage to explain these associations. Methods and findings We analysed a nationwide retrospective cohort of all adult inpatients discharged with symptomatic COVID-19 between February 24, 2020 and August 28, 2021 in mainland France. The primary exposure was preexisting mental disorders assessed from all discharge information recorded over the last 9 years (dementia, depression, anxiety disorders, schizophrenia, alcohol use disorders, opioid use disorders, Down syndrome, other learning disabilities, and other disorder requiring psychiatric ward admission). The main outcomes were all-cause mortality and access to salvage therapy (intensive-care unit admission or life-saving respiratory support) assessed at 120 days after recorded COVID-19 diagnosis at hospital. Independent associations were analysed in multivariate logistic models. Of 465,750 inpatients with symptomatic COVID-19, 153,870 (33.0%) were recorded with a history of mental disorders. Almost all categories of mental disorders were independently associated with higher mortality risks (except opioid use disorders) and lower salvage therapy rates (except opioid use disorders and Down syndrome). After taking into account the mortality risk predicted at baseline from patient vulnerability (including older age and severe somatic comorbidities), excess mortality risks due to caseload surges in hospitals were +5.0% (95% confidence interval (CI), 4.7 to 5.2) in patients without mental disorders (for a predicted risk of 13.3% [95% CI, 13.2 to 13.4] at baseline) and significantly higher in patients with mental disorders (+9.3% [95% CI, 8.9 to 9.8] for a predicted risk of 21.2% [95% CI, 21.0 to 21.4] at baseline). In contrast, salvage therapy rates were significantly higher than expected in patients without mental disorders (+4.2% [95% CI, 3.8 to 4.5]) and lower in patients with mental disorders (-4.1% [95% CI, -4.4; -3.7]). The main limitations of our study point to the assessment of COVID-19-related mortality at 120 days and potential coding bias of medical information recorded in hospital claims data, although the main study findings were consistently reproduced in multiple sensitivity analyses. Conclusions COVID-19 patients with mental disorders had lower odds of accessing salvage therapy, suggesting that life-saving measures at French hospitals were disproportionately denied to patients with mental disorders in this exceptional context., Author(s): Michaël Schwarzinger 1,2,*, Stéphane Luchini 3, Miriam Teschl 3, François Alla 1,2, Vincent Mallet 4, Jürgen Rehm 5,6,7,8,9,10 Introduction The Coronavirus Disease 2019 (COVID-19) pandemic may have caused more [...]

Published
2023
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22. Hepatic dysfunction impairs prognosis in critically ill patients with hematological malignancies: A post-hoc analysis of a prospective multicenter multinational dataset

Author

Bisbal, Magali, Darmon, Michael, Saillard, Colombe, Mallet, Vincent, Mouliade, Charlotte, Lemiale, Virginie, Benoit, Dominique, Pene, Frederic, Kouatchet, Achille, Demoule, Alexandre, Vincent, Francois, Nyunga, Martine, Bruneel, Fabrice, Lebert, Christine, Renault, Anne, Meert, Anne-Pascale, Hamidfar, Rebecca, Jourdain, Merce, Azoulay, Elie, and Mokart, Djamel

Published
2021
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23. Liver disease in germline mutations of telomere-related genes: Prevalence, clinical, radiological, pathological features, outcome, and risk factors.

Author

Sidali, Sabrina, Borie, Raphaël, de Fontbrune, Flore Sicre, El Husseini, Kinan, Rautou, Pierre-Emmanuel, Lainey, Elodie, Goria, Odile, Crestani, Bruno, Cadranel, Jacques, Cottin, Vincent, Bunel, Vincent, Dumortier, Jérôme, Jacquemin, Emmanuel, Reboux, Noémi, Hirschi, Sandrine, Bourdin, Arnaud, Meszaros, Magdalena, Dharancy, Sebastien, Hilaire, Sophie, and Mallet, Vincent

Published
2024
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24. Management of Hepatitis B Virus and Hepatitis C Virus Infections in Patients with Cancer Receiving Immune Checkpoint Inhibitors.

Author

Mustafayev, Khalis, Mallet, Vincent, and Torres, Harrys A.

Subjects
*HEPATITIS B virus, *IMMUNE checkpoint inhibitors, *CANCER patients, *PREVENTIVE medicine, *ANTIVIRAL agents
Abstract

Background Patients with cancer with hepatitis B virus (HBV) or hepatitis C virus (HCV) infection are excluded from many cancer clinical trials of immune checkpoint inhibitors (ICIs). Therefore, data are limited regarding the management of HBV and HCV infections in patients with cancer treated with ICIs. To address this gap, we reviewed the literature on management of HBV and HCV infections in patients with cancer receiving ICIs. Methods We searched MEDLINE and PubMed for all original research articles, case reports, and systematic reviews published in English between Jul 2013 and Jul 2023 on patients with cancer with HBV or HCV infection receiving ICIs. Results We found 28 studies (three prospective clinical trials, seven retrospective cohort studies, nine retrospective case series, and nine case reports) that evaluated the safety of ICI therapy in patients with HBV infection and cancer. The overall rate of HBV reactivation was 1.4% (38/2799), and no HBV-related deaths were reported. The frequency of HBV reactivation in patients with chronic and past HBV infections was 2% (35/1667) and 0.3% (3/1132), respectively. The risk of HBV reactivation was significantly higher among patients with chronic HBV infection not receiving antiviral prophylaxis than among those receiving antivirals (17% vs 1%, p < 0.05). Based on high-quality evidence, for patients with chronic HBV infection, antiviral prophylaxis is recommended before ICI therapy initiation. For patients with past HBV infection, monitoring and on-demand antiviral treatment are sufficient. We found 11 studies (five clinical trials, five retrospective studies, and one prospective observational study) that evaluated the safety of ICI therapy in patients with HCV infection and cancer. The overall rate of HCV reactivation was 0.5% (2/387), and no HCV-related deaths were reported. HCV reactivation occurs primarily in patients receiving immunosuppressants for ICI-related toxic effects. ICIs are safe for HCV-infected patients with solid tumors. Conclusions Chronic HBV or HCV infection should not be considered a contraindication for ICI therapy. Specific risk assessment, monitoring, and management strategies are necessary to reduce the risk of ICI-related liver injury in patients with cancer and chronic HBV or HCV infection. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Liver disease in germline mutations of telomere-related genes: Prevalence, clinical, radiological, pathological features, outcome, and risk factors

Author

Sidali, Sabrina, primary, Borie, Raphaël, additional, de Fontbrune, Flore Sicre, additional, El Husseini, Kinan, additional, Rautou, Pierre-Emmanuel, additional, Lainey, Elodie, additional, Goria, Odile, additional, Crestani, Bruno, additional, Cadrane, Jacques, additional, Cottin, Vincent, additional, Bune, Vincent, additional, Dumortier, Jérôme, additional, Jacquemin, Emmanuel, additional, Reboux, Noémi, additional, Hirschi, Sandrine, additional, Bourdin, Arnaud, additional, Meszaros, Magdalena, additional, Dharancy, Sebastien, additional, Hilaire, Sophie, additional, Mallet, Vincent, additional, Reynaud-Gaubert, Martine, additional, Terriou, Louis, additional, Gottrand, Frédéric, additional, Abou Chahla, Wadih, additional, Khan, Jean-Emmanuel, additional, Carrier, Paul, additional, Saliba, Faouzi, additional, Rubbia-Brandt, Laura, additional, Aubert, John-David, additional, Elkrief, Laure, additional, de Lédinghen, Victor, additional, Abergel, Armand, additional, Olivier, Tournilhac, additional, Houssel, Pauline, additional, Jouneau, Stephane, additional, Wemeau, Lidwine, additional, Bergeron, Anne, additional, Leblanc, Thierry, additional, Ollivier-Hourmand, Isabelle, additional, Nguyen Khac, Eric, additional, Morisse-Pradier, Hélène, additional, Ba, Ibrahima, additional, Boileau, Catherine, additional, Roudot-Thoraval, Françoise, additional, Vilgrain, Valérie, additional, Bureau, Christophe, additional, Nunes, Hilario, additional, Naccache, Jean-Marc, additional, Durand, François, additional, Francoz, Claire, additional, Roulot, Dominique, additional, Valla, Dominique, additional, Paradis, Valérie, additional, Kannengiesser, Caroline, additional, and Plessier, Aurélie, additional

Published
2023
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29. Contributing factors and outcomes of burn-associated cholestasis

Author

de Tymowski, Christian, Dépret, François, Soussi, Sabri, Nabila, Moreno, Vauchel, Thomas, Chaussard, Maité, Benyamina, Mourad, Ferry, Axelle, Cupaciu, Alexandru, Jully, Marion, Oueslati, Haikel, Fratani, Alexandre, Coutrot, Maxime, Sogni, Philippe, Mimoun, Maurice, Chaouat, Marc, Zagdanski, Anne-Marie, De Kerviler, Eric, Mebazaa, Alexandre, Moreau, Richard, Mallet, Vincent, and Legrand, Matthieu

Published
2019
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32. Burden of grade 3 or 4 liver injury associated with immune checkpoint inhibitors

Author

Parlati, Lucia, primary, Sakka, Mehdi, additional, Retbi, Aurelia, additional, Bouam, Samir, additional, Hassani, Lamia, additional, Meritet, Jean-François, additional, Rufat, Pierre, additional, Bonnefont-Rousselot, Dominique, additional, Batista, Rui, additional, Terris, Benoit, additional, Bellanger, Agnès, additional, Thabut, Dominique, additional, Vozy, Aurore, additional, Spano, Jean-Philippe, additional, Coriat, Romain, additional, Goldwasser, François, additional, Aractingi, Selim, additional, Sogni, Philippe, additional, Pol, Stanislas, additional, and Mallet, Vincent, additional

Published
2023
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38. Performance of spleen stiffness measurement by vibration-controlled transient elastography to rule out high-risk varices in patients with porto-sinusoidal vascular disorder

Author

Moga, Lucile, primary, Paradis, Valérie, additional, Gudavalli, Koushik, additional, Silva, Joel, additional, Colecchia, Antonio, additional, Ravaioli, Federico, additional, Nicoara-Farcau, Oana, additional, Tosetti, Giulia, additional, Berzigotti, Annalisa, additional, Procopet, Bogdan, additional, Simón-Talero, Macarena, additional, Turco, Laura, additional, Capinha, Francisco, additional, Elkrief, Laure, additional, Ferrusquia Acosta, Jose Alberto, additional, Goria, Odile, additional, Balcar, Lorenz, additional, Adrien, Lannes, additional, Mallet, Vincent, additional, Poujol-Robert, Armelle, additional, Thabut, Dominique, additional, Houssel-Debry, Pauline, additional, Wong, Yu Jun, additional, Reiberger, Thomas, additional, Monllor-Nunell, Teresa, additional, Vitale, Giovanni, additional, Ferreira, Carlos Noronha, additional, Vidal-González, Judit, additional, Fodor, Andreea, additional, Antonenko, Antonina, additional, Caccia, Riccardo, additional, Turon, Fanny, additional, Dajti, Elton, additional, Schepis, Filippo, additional, Indulti, Federica, additional, Macedo, Guilherme, additional, Rampally, Sai Prasanth, additional, Payancé, Audrey, additional, Laurent, Castera, additional, Valsan, Arun, additional, Plessier, Aurélie, additional, and Rautou, Pierre-Emmanuel, additional

Published
2023
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40. Identification of clinical and inherited genetic variants associated with severe complications of chronic liver diseases

Author

Trepo, Eric, Moreno, Christophe, Verhoeven, Caroline, Migeotte, Isabelle, Donckier De Donceel, Vincent, Eisendrath, Pierre, Thévenot, Thierry, Mallet, Vincent, Otero Sanchez, Lukas, Trepo, Eric, Moreno, Christophe, Verhoeven, Caroline, Migeotte, Isabelle, Donckier De Donceel, Vincent, Eisendrath, Pierre, Thévenot, Thierry, Mallet, Vincent, and Otero Sanchez, Lukas

Abstract

Chronic liver disease (CLD) accounts for 2 million deaths yearly worldwide. Fatty liver disease (FLD) is the most prevalent cause of CLD and includes alcohol-related liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD). FLD affects nearly 30% of the world's population. The two major risk factors for FLD are alcohol misuse and metabolic syndrome, including obesity and diabetes. Among that large population, only a fraction will progress to more severe stages. It includes decompensated cirrhosis and hepatocellular carcinoma (HCC). Decompensated cirrhosis and HCC are highly refractory to therapeutic intervention. Identifying and characterizing at-risk populations for CLD-related complications are needed for proper and impactful prevention strategies and treatment. However, identifying at-risk patients is challenging due to the scarcity of relevant biomarkers. We aim through this thesis to identify risk factors associated with liver-related complications, specifically in patients with diabetes, severe forms of ALD, and decompensated cirrhosis. Firstly, we evaluated whether a novel machine learning-based classification of patients with diabetes could identify individuals with an increased risk of liver-related complications. In this study, we included 1,068 adult patients with diabetes. Using competing-risk analysis, we observed that patients allocated to the severe-insulin resistant (SIR) group and patients with excessive alcohol consumption at baseline have the highest risk for liver-related events. Furthermore, being a member of the SIR group, excessive alcohol consumption and hypertension were independently associated with clinically significant fibrosis, evaluated by liver biopsy or FibroScan. Secondly, we evaluated in another retrospective study whether, in ALD patients, severe alcoholic hepatitis (sAH) modified the risk and severity of infection compared to an alcohol-related cirrhosis (DAC) without sAH. A total of 207 patients were included (139 with, Doctorat en Sciences biomédicales et pharmaceutiques (Médecine), info:eu-repo/semantics/nonPublished

Published
2023

42. Oral Zinc Supplementation in Chronically HEV-Infected Patients Not Responding to Ribavirin Monotherapy

Author

Horvatits, Thomas, primary, Behrendt, Patrick, additional, Schuebel, Niels, additional, Guthoff, Martina, additional, Wiegand, Johannes, additional, Harth, Anna, additional, Mersi, Julia, additional, Luetgehetmann, Marc, additional, Gallon, Clemence, additional, Rybczynski, Meike, additional, Liang, Zhaochao, additional, Maasoumy, Benjamin, additional, Mallet, Vincent, additional, Wang, Lin, additional, and Pischke, Sven, additional

Published
2023
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43. Germline Mutations of Telomere-Related Genes are a Major Risk Factor for Liver Disease: A Multicentric Transversal Study

Author

Sidali, Sabrina, primary, Borie, Raphaël, additional, Sicre de Fontbrune, Flore, additional, El Husseini, Kinan, additional, Rautou, Pierre-Emmanuel, additional, Lainey, Elodie, additional, Goria, Odile, additional, Crestani, Bruno, additional, Cadranel, Jacques, additional, Cottin, Vincent, additional, Bunel, Vincent, additional, Dumortier, Jérôme, additional, Jacquemin, Emmanuel, additional, Reboux, Noémi, additional, Hirschi, Sandrine, additional, Bourdin, Arnaud, additional, Meszaros, Magdalena, additional, Dharancy, Sébastien, additional, Hilaire, Sophie, additional, Mallet, Vincent, additional, Reynaud-Gaubert, Martine, additional, Terriou, Louis, additional, Gottrand, Frédéric, additional, Abou Chahla, Wadih, additional, Kahn, Jean-Emmanuel, additional, Carrier, Paul, additional, Saliba, Faouzi, additional, Rubbia-Brandt, Laura, additional, Aubert, John-David, additional, Elkrief, Laure, additional, de Ledinghen, Victor, additional, Abergel, Armand, additional, Tournilhac, Olivier, additional, Houssel, Pauline, additional, Jouneau, Stéphane, additional, Wemeau, Ludivine, additional, Bergeron, Anne, additional, Leblanc, Thierry, additional, Ollivier-Hourmand, Isabelle, additional, Nguyen-Khac, Eric, additional, Morisse-Pradier, Hélène, additional, Ba, Ibrahima, additional, Boileau, Catherine, additional, Roulot-Thoraval, Françoise, additional, Vilgrain, Valérie, additional, Bureau, Christophe, additional, Nunes, Hilario, additional, Naccache, Jean-Marc, additional, Durand, François, additional, Francoz, Claire, additional, Roulot, Dominique, additional, Valla, Dominique-Charles, additional, Paradis, Valérie, additional, Kannengiesser, Caroline, additional, and Plessier, Aurélie, additional

Published
2023
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48. Apprentissage profond géométrique pour la bioinformatique structurale

Author

Mallet, Vincent, Bioinformatique structurale - Structural Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université Paris Cité, Michaël Nilges, Jean-Philippe Vert, and STAR, ABES

Subjects
Structural bioinformatics, Apprentissage profond géométrique, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Geometric deep learning, Drug design, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

Machine learning has enabled several breakthroughs in tabular, image and text data management. It has also started to help science, for instance Alphafold, but its application to scientific questions is not necessarily straightforward. The first challenge is to model natural objects as mathematical objects that can exist in a computer - like pictures - while respecting their physical properties. The second challenge is to expand machine learning methods to work on mathematical and numerical objects with more structure, in a field known as geometric deep learning. Having a wider range of mathematical objects gives us more freedom to efficiently represent our natural objects for machine learning. Structural biology is a scientific field aiming at understanding life by using the 3D structures of important molecules, available thanks to experimental and computational tools. This field thus relies on structured data that could be amenable to machine learning if the two challenges above were addressed. Among the major applications of this structural biology is drug discovery that aims at finding potential drugs in a vast space of chemical compounds. In the target-centric approach, the 3D structures of target molecules are used to select these promising compounds. This approach could be revolutionized by the use of geometric deep learning. We start by a methodological contribution that helps respect the structure of DNA molecules represented as strings. Indeed, such representation traditionally overlooks the reverse-complement symmetry that stems from the double-stranded nature of DNA. By using the theory of equivariance, we characterize the class of machine learning models that respect this additional structure. We empirically show that using this class of models enhances the accuracy of the prediction of transcription factor binding. We then advocate for the use of a specific type of graph to represent RNA in conjunction with deep learning methods for graphs. This specific type of graph is a coarsened, discrete representation that was introduced by biochemists twenty years ago. We show that using this representation is superior to using basic graphs and sufficient to extract a signal relevant to RNA drug discovery. Moreover, we can leverage this learning framework to efficiently detect structural motifs in RNA, relaxing constraints imposed on these motifs by pre-existing tools. We release a package to use this representation in machine learning applications. Finally, we introduce three tools to help target-centric drug discovery that rely on machine learning. We offer a tool that is dedicated to finding the binding sites at protein-protein interaction sites by simultaneously predicting small-molecules and protein binding. We also propose a tool to efficiently cluster together conformations from a molecular dynamics trajectory, enabling for selection of relevant representative conformations. Lastly, we propose a method that iteratively generates populations of compounds with higher and higher predicted affinity to a given target., L'apprentissage automatique a permis plusieurs percées dans la gestion des données tabulaires, d'images ou de texte. Il a également commencé à aider la science, par exemple avec Alphafold, mais son application aux questions scientifiques n'est pas immédiate. Le premier défi consiste à modéliser des objets naturels avec des objets mathématiques représentés dans un ordinateur - comme des images - tout en respectant leurs propriétés physiques. Le deuxième défi est d'étendre les méthodes d'apprentissage à de nouveaux objets mathématiques et numériques avec plus de structure, un domaine de recherche connu sous le nom d'apprentissage profond géométrique. Avoir un éventail plus large d'objets mathématiques nous donne plus de liberté pour modéliser efficacement nos objets naturels pour l'apprentissage automatique. La biologie structurale est un domaine scientifique visant à comprendre le vivant en utilisant les structures tridimensionnelles de molécules importantes, disponibles grâce à des outils expérimentaux et informatiques. Ce domaine s'appuie donc sur des données structurées qui pourraient se prêter à l'apprentissage automatique si les deux défis ci-dessus étaient relevés. Parmi les principales applications de la biologie structurale figure la découverte de médicaments, qui vise à trouver de potentiels médicaments dans un vaste espace de composés chimiques. Dans l'approche centrée sur les cibles thérapeutiques, les structures tridimensionnelles de celles-ci sont utilisées pour sélectionner ces potentiels médicaments. Cette approche pourrait être révolutionnée par l'utilisation de l'apprentissage profond géométrique. Nous commençons par un apport méthodologique qui permet de respecter la structure des molécules d'ADN représentées sous forme de chaînes de caractères. En effet, une telle représentation néglige la symétrie du brin complémentaire qui découle de l'appariement des deux brins de l'ADN. En utilisant la théorie de l'équivariance, nous caractérisons la classe de modèles d'apprentissage automatique qui respectent cette structure supplémentaire. Nous montrons empiriquement qu'utiliser cette classe de modèles améliore la précision de la prédiction de la liaison des facteurs de transcription. Nous préconisons ensuite l'utilisation d'un type spécifique de graphe pour représenter l'ARN en conjonction avec des méthodes d'apprentissage profond pour les graphes. Ce type spécifique de graphes est une représentation gros-grain et discrète introduite par les biochimistes il y a vingt ans. Nous montrons que l'utilisation de cette représentation est supérieure à l'utilisation de graphes de base et suffisante pour extraire un signal pertinent pour la découverte de médicaments ciblant l'ARN. De plus, nous pouvons tirer parti de ce cadre d'apprentissage pour détecter efficacement des motifs structuraux dans l'ARN, en relâchant les contraintes imposées à ces motifs par les outils préexistants. Nous avons publié un package pour utiliser cette représentation dans les applications d'apprentissage automatique. Enfin, nous présentons trois outils pour aider à la découverte de médicaments centrés sur les cibles thérapeutiques qui reposent sur l'apprentissage automatique. Nous proposons un outil dédié à la recherche de sites de liaison aux sites d'interaction protéine-protéine en prédisant simultanément la liaison aux petites molécules et aux protéines. Nous proposons également un outil pour regrouper efficacement les conformations d'une trajectoire de dynamique moléculaire, permettant la sélection de conformations représentatives pertinentes. Enfin, nous proposons une méthode qui génère des populations de composés avec une affinité accrue pour une cible donnée.

Published
2022

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