Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

<h4>Background</h4>Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately...

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Main Authors: Naeimeh Atabaki-Pasdar, Mattias Ohlsson, Ana Viñuela, Francesca Frau, Hugo Pomares-Millan, Mark Haid, Angus G Jones, E Louise Thomas, Robert W Koivula, Azra Kurbasic, Pascal M Mutie, Hugo Fitipaldi, Juan Fernandez, Adem Y Dawed, Giuseppe N Giordano, Ian M Forgie, Timothy J McDonald, Femke Rutters, Henna Cederberg, Elizaveta Chabanova, Matilda Dale, Federico De Masi, Cecilia Engel Thomas, Kristine H Allin, Tue H Hansen, Alison Heggie, Mun-Gwan Hong, Petra J M Elders, Gwen Kennedy, Tarja Kokkola, Helle Krogh Pedersen, Anubha Mahajan, Donna McEvoy, Francois Pattou, Violeta Raverdy, Ragna S Häussler, Sapna Sharma, Henrik S Thomsen, Jagadish Vangipurapu, Henrik Vestergaard, Leen M 't Hart, Jerzy Adamski, Petra B Musholt, Soren Brage, Søren Brunak, Emmanouil Dermitzakis, Gary Frost, Torben Hansen, Markku Laakso, Oluf Pedersen, Martin Ridderstråle, Hartmut Ruetten, Andrew T Hattersley, Mark Walker, Joline W J Beulens, Andrea Mari, Jochen M Schwenk, Ramneek Gupta, Mark I McCarthy, Ewan R Pearson, Jimmy D Bell, Imre Pavo, Paul W Franks
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-06-01
Series:PLoS Medicine
Online Access:https://doi.org/10.1371/journal.pmed.1003149

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https://doi.org/10.1371/journal.pmed.1003149

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