Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients

Approximately 30% of psoriasis patients develop psoriatic arthritis (PsA) and early diagnosis is crucial for the management of PsA. Here, Patrick et al. develop a computational pipeline involving statistical and machine-learning methods that can assess the risk of progression to PsA based on genetic...

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Autores principales: Matthew T. Patrick, Philip E. Stuart, Kalpana Raja, Johann E. Gudjonsson, Trilokraj Tejasvi, Jingjing Yang, Vinod Chandran, Sayantan Das, Kristina Callis-Duffin, Eva Ellinghaus, Charlotta Enerbäck, Tõnu Esko, Andre Franke, Hyun M. Kang, Gerald G. Krueger, Henry W. Lim, Proton Rahman, Cheryl F. Rosen, Stephan Weidinger, Michael Weichenthal, Xiaoquan Wen, John J. Voorhees, Gonçalo R. Abecasis, Dafna D. Gladman, Rajan P. Nair, James T. Elder, Lam C. Tsoi
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2018
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Acceso en línea:https://doaj.org/article/f67954f1b8fa41a1be488a5ddddc158b
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Sumario:Approximately 30% of psoriasis patients develop psoriatic arthritis (PsA) and early diagnosis is crucial for the management of PsA. Here, Patrick et al. develop a computational pipeline involving statistical and machine-learning methods that can assess the risk of progression to PsA based on genetic markers.