Results 171 to 180 of about 283,327 (339)
Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study
, 2022 C.G. Downie, S.F. Dimos, S.A. Bien, Y. Hu, B.F. Darst, L.M. Polfus, Y. Wang, G.L. Wojcik, R. Tao, L.M. Raffield, N.D. Armstrong, H.G. Polikowsky, J.E. Below, A. Correa, M.R. Irvin, L.J.F. Rasmussen-Torvik, C.S. Carlson, L.S. Phillips, S. Liu, J.S. Pankow, S.S. Rich, J.I. Rotter, S. Buyske, T.C. Matise, K.E. North, C.L. Avery, C.A. Haiman, R.J.F. Loos, C. Kooperberg, M. Graff, H.M. Highland +30 moreopenalex +1 more sourceSingle-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders [PDF]
, 2021 Yongjin Park, Liang He, José Dávila-Velderrain, Lei Hou, Shahin Mohammadi, Hansruedi Mathys, Zhuyu Peng, David A. Bennett, Li‐Huei Tsai, Manolis Kellis +9 moreopenalex +1 more sourceGenetic Overlap Between Obstructive Sleep Apnea and Ischemic Stroke: A Large-Scale Genome-Wide Cross-Trait Analysis
Nature and Science of SleepWanqing Lin,1,2,* Zhiyi Zhang,3,* Chenlin Wang,4 Yingling Ye,4 Lingrong Zheng,4 Qianqian Hu,4 Renyu Yu,1 Mingxia Wu,4,5 Bin Chen1 1Department of Rehabilitation Medicine and National Clinical Research Base of Traditional Chinese Medicine, The ...Lin W, Zhang Z, Wang C, Ye Y, Zheng L, Hu Q, Yu R, Wu M, Chen B +8 moredoaj Susceptible genes and disease mechanisms identified in frontotemporal dementia and frontotemporal dementia with Amyotrophic Lateral Sclerosis by DNA-methylation and GWAS [PDF]
, 2017 Erdogan Taskesen, Aniket Mishra, Sophie van der Sluis, Raffaele Ferrari, D. G. Hernandez, M. A. Nalls, Jonathan D. Rohrer, Adaikalavan Ramasamy, John B. Kwok, Carol Dobson‐Stone, Peter R. Schofield, Glenda M. Halliday, J. R. Hodges, Olivier Piguet, Lauren Bartley, Emma E. Thompson, Eric Haan, Israel Alejandro Quijano-Hernández, Agustı́n Ruiz, Merçé Boada, Barbara Borroni, Alessandro Padovani, Carlos Cruchaga, Nigel J. Cairns, Luisa Benussi, Giuliano Binetti, Roberta Ghidoni, Gianluigi Forloni, Diego Albani, Daniela Galimberti, Chiara Fenoglio, María Serpente, Elio Scarpini, Jordi Clarimón, Alberto Lleó, Rafael Blesa, María Landqvist Waldö, Kristina Nilsson, Christer Nilsson, I. R. A. Mackenzie, Ging‐Yuek Robin Hsiung, David Mann, Jordan Grafman, Christopher M. Morris, Johannes Attems, Timothy D. Griffiths, Ian G. McKeith, Alan Thomas, Pietro Pietrini, Edward D. Huey, Eric M. Wassermann, Atik Baborie, Evelyn Jaros, Michael Tierney, Pau Pástor, Cristina Razquín, Sara Ortega‐Cubero, Elena Alonso, Robert Perneczky, Janine Diehl‐Schmid, Panagiotis Alexopoulos, Andrea Kurz, Innocenzo Rainero, Elisa Rubino, Lorenzo Pinessi, Ekaterina Rogaeva, Peter St George‐Hyslop, Giacomina Rossi, Fabrizio Tagliavini, Giorgio Giaccone, James B. Rowe, Johannes C. M. Schlachetzki, James Uphill, J. Collinge, Simon Mead, Adrian Danek, Vivianna M. Van Deerlin, Murray Grossman, John Q. Trojanowski, Julie van der Zee, Christine Van Broeckhoven, Stefano F. Cappa, Isabelle Leber, Didier Hannequin, Véronique Golfier, Martine Vercelletto, Alexis Brice, Benedetta Nacmias, Sandro Sorbi, Silvia Bagnoli, Irene Piaceri, Jens Høiriis Nielsen, L. E. Hjermind, Markus J. Riemenschneider, Manuel Mayhaus, Bernd Ibach, Gilles Gasparoni, Sabrina Pichler, Wei Gu, Martin N. Rossor +99 moreopenalex +1 more sourceDeep phenotyping of heart failure with preserved ejection fraction through multi‐omics integration
European Journal of Heart Failure, EarlyView.Deep phenotyping of of heart failure with preserved ejection fraction (HFpEF) through multi‐omics integration. AI, artificial intelligence. Aims
Heart failure with preserved ejection fraction (HFpEF) has become the predominant form of heart failure and a leading cause of global cardiovascular morbidity and mortality.Jakob Versnjak, Titus Kuehne, Pauline Fahjen, Nina Jovanovic, Ulrike Löber, Gabriele G. Schiattarella, Nicola Wilck, Holger Gerhardt, Dominik N. Müller, Frank Edelmann, Philipp Mertins, Roland Eils, Michael Gotthardt, Sofia K. Forslund, Benjamin Wild, Marcus Kelm +15 morewiley +1 more sourceUnsupervised machine learning for cardiovascular disease: A framework for future studies
European Journal of Heart Failure, EarlyView.Unsupervised machine learning can improve the characterization and stratification of patients with cardiovascular diseases (CVDs). Clustering algorithms, which group patients based on patterns in clinical data, can reveal distinct subgroups that may differ in prognosis and treatment response.Emmanuel Bresso, Claire Lacomblez, Kévin Duarte, Luca Monzo, Guillaume Baudry, Jasper Tromp, Abhinav Sharma, Nicolas Girerd +7 morewiley +1 more source