Search

Your search keyword '"Davtian, David"' showing total 14 results
Start Over Author "Davtian, David"
14 results on '"Davtian, David"'

1. Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

Author

Brown, Andrew A., Fernandez-Tajes, Juan J., Hong, Mun-gwan, Brorsson, Caroline A., Koivula, Robert W., Davtian, David, Dupuis, Théo, Sartori, Ambra, Michalettou, Theodora-Dafni, Forgie, Ian M., Adam, Jonathan, Allin, Kristine H., Caiazzo, Robert, Cederberg, Henna, De Masi, Federico, Elders, Petra J. M., Giordano, Giuseppe N., Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison J., Howald, Cédric, Jones, Angus G., Kokkola, Tarja, Laakso, Markku, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Mourby, Miranda, Musholt, Petra B., Nilsson, Birgitte, Pattou, Francois, Penet, Deborah, Raverdy, Violeta, Ridderstråle, Martin, Romano, Luciana, Rutters, Femke, Sharma, Sapna, Teare, Harriet, ‘t Hart, Leen, Tsirigos, Konstantinos D., Vangipurapu, Jagadish, Vestergaard, Henrik, Brunak, Søren, Franks, Paul W., Frost, Gary, Grallert, Harald, Jablonka, Bernd, McCarthy, Mark I., Pavo, Imre, Pedersen, Oluf, Ruetten, Hartmut, Walker, Mark, Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., and Viñuela, Ana

Published
2023
Full Text
View/download PDF

2. Differential expression analyses on aortic tissue reveal novel genes and pathways associated with abdominal aortic aneurysm onset and progression

Author

Temprano-Sagrera, Gerard, primary, Soto, Begoña, additional, Dilmé, Jaume, additional, Peypoch, Olga, additional, Calsina-Juscafresa, Laura, additional, Davtian, David, additional, Nieto, Lluis, additional, Brown, Andrew, additional, Escudero, José-Román, additional, Viñuela, Ana, additional, Camacho, Mercedes, additional, and Sabater-Lleal, Maria, additional

Published
2024
Full Text
View/download PDF

4. Discovery of Type 2 Diabetes genes using an accessible tissue

Author

Davtian, David, Schwenk, Jochen M., McCarthy, Mark, Mahajan, Anubha, Hong, Mun-Gwan, Dermitzakis, Emmanouil, Im, Hae Kyung, Pearson, Ewan, Vinuela, Ana, Brown, Andrew, Davtian, David, Schwenk, Jochen M., McCarthy, Mark, Mahajan, Anubha, Hong, Mun-Gwan, Dermitzakis, Emmanouil, Im, Hae Kyung, Pearson, Ewan, Vinuela, Ana, and Brown, Andrew

Abstract

QC 20231121

Published
2023

5. Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models

Author

Allesøe, Rosa Lundbye, Lundgaard, Agnete Troen, Hernández Medina, Ricardo, Aguayo-Orozco, Alejandro, Johansen, Joachim, Nissen, Jakob Nybo, Brorsson, Caroline, Mazzoni, Gianluca, Niu, Lili, Biel, Jorge Hernansanz, Brasas, Valentas, Webel, Henry, Benros, Michael Eriksen, Pedersen, Anders Gorm, Chmura, Piotr Jaroslaw, Jacobsen, Ulrik Plesner, Mari, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Hong, Mun-Gwan, Musholt, Petra B., de Masi, Federico, Vogt, Josef, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, E. Louise, Frost, Gary, Thomsen, Henrik, Hansen, Elizaveta, Hansen, Tue Haldor, Vestergaard, Henrik, Muilwijk, Mirthe, Blom, Marieke T., ‘t Hart, Leen M., Pattou, Francois, Raverdy, Violeta, Brage, Soren, Kokkola, Tarja, Heggie, Alison, McEvoy, Donna, Mourby, Miranda, Kaye, Jane, Hattersley, Andrew, McDonald, Timothy, Ridderstråle, Martin, Walker, Mark, Forgie, Ian, Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Pedersen, Oluf, Hansen, Torben, Dermitzakis, Emmanouil, Franks, Paul W., Schwenk, Jochen M., Adamski, Jerzy, McCarthy, Mark I., Pearson, Ewan, Banasik, Karina, Rasmussen, Simon, Brunak, S. ren, Froguel, Philippe, Thomas, Cecilia Engel, Haussler, Ragna, Beulens, Joline, Rutters, Femke, Nijpels, Giel, van Oort, Sabine, Groeneveld, Lenka, Elders, Petra, Giorgino, Toni, Rodriquez, Marianne, Nice, Rachel, Perry, Mandy, Bianzano, Susanna, Graefe-Mody, Ulrike, Hennige, Anita, Grempler, Rolf, Baum, Patrick, Stærfeldt, Hans-Henrik, Shah, Nisha, Teare, Harriet, Ehrhardt, Beate, Tillner, Joachim, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinevich, Iryna, Cabrelli, Louise, Loftus, Heather, Bizzotto, Roberto, Tura, Andrea, Dekkers, Koen, van Leeuwen, Nienke, Groop, Leif, Slieker, Roderick, Ramisch, Anna, Jennison, Christopher, McVittie, Ian, Frau, Francesca, Steckel-Hamann, Birgit, Adragni, Kofi, Thomas, Melissa, Pasdar, Naeimeh Atabaki, Fitipaldi, Hugo, Kurbasic, Azra, Mutie, Pascal, Pomares-Millan, Hugo, Bonnefond, Amelie, Canouil, Mickael, Caiazzo, Robert, Verkindt, Helene, Holl, Reinhard, Kuulasmaa, Teemu, Deshmukh, Harshal, Cederberg, Henna, Laakso, Markku, Vangipurapu, Jagadish, Dale, Matilda, Thorand, Barbara, Nicolay, Claudia, Fritsche, Andreas, Hill, Anita, Hudson, Michelle, Thorne, Claire, Allin, Kristine, Arumugam, Manimozhiyan, Jonsson, Anna, Engelbrechtsen, Line, Forman, Annemette, Dutta, Avirup, Sondertoft, Nadja, Fan, Yong, Gough, Stephen, Robertson, Neil, McRobert, Nicky, Wesolowska-Andersen, Agata, Brown, Andrew, Davtian, David, Dawed, Adem, Donnelly, Louise, Palmer, Colin, White, Margaret, Ferrer, Jorge, Whitcher, Brandon, Artati, Anna, Prehn, Cornelia, Adam, Jonathan, Grallert, Harald, Gupta, Ramneek, Sackett, Peter Wad, Nilsson, Birgitte, Tsirigos, Konstantinos, Eriksen, Rebeca, Jablonka, Bernd, Uhlen, Mathias, Gassenhuber, Johann, Baltauss, Tania, de Preville, Nathalie, Klintenberg, Maria, Abdalla, Moustafa, Lundgaard, Agnete Troen [0000-0001-7447-6560], Hernández Medina, Ricardo [0000-0001-6373-2362], Johansen, Joachim [0000-0001-7052-1870], Niu, Lili [0000-0003-4571-4368], Biel, Jorge Hernansanz [0000-0002-3125-2951], Benros, Michael Eriksen [0000-0003-4939-9465], Pedersen, Anders Gorm [0000-0001-9650-8965], Jacobsen, Ulrik Plesner [0000-0001-9181-6854], Koivula, Robert [0000-0002-1646-4163], Vinuela, Ana [0000-0003-3771-8537], Haid, Mark [0000-0001-6118-1333], Hong, Mun-Gwan [0000-0001-8603-8293], Kennedy, Gwen [0000-0002-9856-3236], Thomas, E Louise [0000-0003-4235-4694], Frost, Gary [0000-0003-0529-6325], Hansen, Tue Haldor [0000-0001-5948-8993], Kaye, Jane [0000-0002-7311-4725], Hattersley, Andrew [0000-0001-5620-473X], Ridderstråle, Martin [0000-0002-3270-9167], Pedersen, Oluf [0000-0002-3321-3972], Hansen, Torben [0000-0001-8748-3831], Schwenk, Jochen M [0000-0001-8141-8449], Rasmussen, Simon [0000-0001-6323-9041], Brunak, Søren [0000-0003-0316-5866], Apollo - University of Cambridge Repository, Epidemiology and Data Science, ACS - Diabetes & metabolism, APH - Health Behaviors & Chronic Diseases, General practice, ACS - Heart failure & arrhythmias, APH - Aging & Later Life, Graduate School, and APH - Methodology

Subjects
Biomedical Engineering, Type 2 diabetes, Bioengineering, Applied Microbiology and Biotechnology, Deep Learning, SDG 3 - Good Health and Well-being, Diabetes Mellitus, Type 2, Machine learning, Molecular Medicine, Humans, Data integration, IMI DIRECT Consortium, Systems biology, Algorithms, Biotechnology
Abstract

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Published
2023
Full Text
View/download PDF

12. An individual based model to optimize natural enemies deployment in augmentative biological control

Author

Mailleret, Ludovic, Davtian, David, Grognard, Frédéric, Institut Sophia Agrobiotech (ISA), Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Recherche Agronomique (INRA), Biological control of artificial ecosystems (BIOCORE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire International de Recherche en Informatique et Mathématiques Appliquées (LIRIMA), Centre National de la Recherche Scientifique et Technologique (CNRST)-Université Gaston Bergé Sénégal-Université d'Antananarivo-Université Joseph Ki-Zerbo [Ouagadougou] (UJZK)-Université Badji Mokhtar - Annaba [Annaba] (UBMA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Yaoundé I, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), EPITAG, Institut National de la Recherche Agronomique (INRA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Université de Yaoundé I-Université Badji Mokhtar Annaba (UBMA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Joseph Ki-Zerbo [Ouagadougou] (UJZK)-Université d'Antananarivo-Université Gaston Bergé Sénégal-Centre National de la Recherche Scientifique et Technologique (CNRST), Institut Sophia Agrobiotech [Sophia Antipolis] (ISA), Institut National de la Recherche Agronomique (INRA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Université de Yaoundé I [Yaoundé]-Université Badji Mokhtar - Annaba [Annaba] (UBMA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Ouagadougou-Université d'Antananarivo-Université Gaston Bergé Sénégal-Centre National de la Recherche Scientifique et Technologique (CNRST), and Université Côte d'Azur (UCA)

Subjects
[SDV.EE]Life Sciences [q-bio]/Ecology, environment, Population dynamics, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Biological control, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], Netlogo, [SDV.SA.HORT]Life Sciences [q-bio]/Agricultural sciences/Horticulture
Abstract

International audience; Augmentative biological control is a crop protection method that relies on the repeated introduction of natural enemies to fight agricultural crop pests. The question of the amount, distribution and frequency of natural enemies introductions to best suppress the pests is a central issue. Mathematical results were obtained with hybrid population dynamics models. They indicate that the optimal deployment strategy of natural enemies strongly relies on the presence [4] and sign of density dependence among the natural enemies population [5, 6, 1], and is also affected by the spatial structure of the environment [3]. To evaluate these theoretical predictions in a more realistic, stochastic and spatially explicit setting, a stochastic individual based model has been built on the multi-agent programmable modeling environment Netlogo [7]. Extensive simulatory experiments were performed to assess the effects of density dependent processes as well as spatial structure and stochasticity on augmentative biological control performance and variability. In addition to being used to optimise biological control agents introductions, the model has also been designed to ease the communication with a non-specialist audience regarding the effects of complex population dynamics processes on augmentative biological control efficacy and optimal natural enemies deployment strategies. This objective ressembles that of the Webidemics model in plant epidemics control [2].References[1] N. Bajeux, F. Grognard, and L. Mailleret. Augmentative biological control when the natural enemies are subject to Allee effects. Journal of Mathematical Biology, Vol 47-1, pp 1561-1587, 2017.[2] N.J. Cunniffe , R.O.J.H. Stutt, R.E. DeSimone, T.R. Gottwald, C.A. Gilligan. Optimising and communicating options for the control of invasive plant disease when there is epidemiological uncertainty. PLoS Computational Biology, 2015.[3] B. Ghosh, F. Grognard and L. Mailleret. Natural enemies deployment in patchy environments for augmentative biological control. Applied Mathematics and Computations, Vol. 266, pp. 982-999, 2015.[4] L. Mailleret and F. Grognard. Global stability and optimisation of a general impulsive biological control model. Mathematical Biosciences, Vol. 221-2, pp. 91-100, 2009.[5] S. Nundloll, L. Mailleret and F. Grognard. Influence of intrapredatory interferences on impulsive biological control efficiency. Bulletin of Mathematical Biology, Vol. 72-8, pp. 2113-2138, 2010.[6] S. Nundloll, L. Mailleret and F. Grognard. Two models of interfering predators in impulsive biological control. Journal of Biological Dynamics, Vol. 4, pp. 102-114, 2010.[7] U. Wilensky, 1999. NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Evanston, IL.

Published
2018

14. Differential Expression Analyses on Human Aortic Tissue Reveal Novel Genes and Pathways Associated With Abdominal Aortic Aneurysm Onset and Progression.

Author

Temprano-Sagrera G, Peypoch O, Soto B, Dilmé J, Calsina Juscafresa L, Davtian D, de la Rosa Estadella M, Nieto L, Brown A, Escudero JR, Viñuela A, Camacho M, and Sabater-Lleal M

Subjects
Humans, Male, Female, Aged, Aorta, Abdominal pathology, Aorta, Abdominal metabolism, Gene Expression Profiling methods, Case-Control Studies, Middle Aged, Transcriptome, Genetic Predisposition to Disease, RNA-Seq, Gene Expression Regulation, Aortic Aneurysm, Abdominal genetics, Aortic Aneurysm, Abdominal metabolism, Disease Progression
Abstract

Background: Abdominal aortic aneurysms (AAAs) are focal dilatations of the abdominal aorta that expand progressively, increasing their risk of rupture. Rupture of an AAA is associated with high mortality rates, but the mechanisms underlying the initiation, expansion, and rupture of AAAs are not yet fully understood. We aimed to characterize the pathophysiology of AAAs and identify new genes associated with AAA initiation and progression., Methods and Results: This study used RNA sequencing data on 140 samples, becoming the largest RNA sequencing data set for differential expression studies of AAAs. We performed differential expression analyses and analyses of differential splicing between dilated and nondilated aortic tissue samples, and between AAAs of different diameters. We identified 3002 differentially expressed genes between AAAs and controls that were independent of ischemic time, 1425 of which were new. Additionally, 8 genes ( EXTL3 , ZFR , DUSP8 , DISP1 , USP33 , VPS37C , ZNF784 , RFX1 ) were differentially expressed between AAAs of varying diameters and between AAAs and control samples. Finally, 7 genes ( SPP1 , FHL1 , GNAS , MORF4L2 , HMGN1 , ARL1 , RNASE4 ) with differential splicing patterns were also differentially expressed genes between AAAs and controls, suggesting that splicing differences in these genes may contribute to the observed expression changes and disease development., Conclusions: This study identifies new genes and splicing patterns associated with AAAs and validates previous relevant pathways on AAAs. These findings contribute to the understanding of the complex mechanisms underlying AAAs and may provide potential targets to limit AAA progression and mortality risk.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results