Artificial intelligence-driven genotype–epigenotype–phenotype approaches to resolve challenges in syndrome diagnosticsResearch in context

Artificial intelligence-driven genotype–epigenotype–phenotype approaches to resolve challenges in syndrome diagnosticsResearch in context

Summary: Background: Decisions to split two or more phenotypic manifestations related to genetic variations within the same gene can be challenging, especially during the early stages of syndrome discovery. Genotype-based diagnostics with artificial intelligence (AI)-driven approaches using next-ge...

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Main Authors: Christopher C.Y. Mak, Hannah Klinkhammer, Sanaa Choufani, Nikola Reko, Angela K. Christman, Elise Pisan, Martin M.C. Chui, Mianne Lee, Fiona Leduc, Jennifer C. Dempsey, Pedro A. Sanchez-Lara, Hannah M. Bombei, John A. Bernat, Laurence Faivre, Frederic Tran Mau-Them, Irene Valenzuela Palafoll, Natalie Canham, Ajoy Sarkar, Yuri A. Zarate, Bert Callewaert, Ewelina Bukowska-Olech, Aleksander Jamsheer, Andreas Zankl, Marjolaine Willems, Laura Duncan, Bertrand Isidor, Benjamin Cogne, Odile Boute, Clémence Vanlerberghe, Alice Goldenberg, Elliot Stolerman, Karen J. Low, Vianney Gilard, Jeanne Amiel, Angela E. Lin, Christopher T. Gordon, Dan Doherty, Peter M. Krawitz, Rosanna Weksberg, Tzung-Chien Hsieh, Brian H.Y. Chung
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:EBioMedicine
Subjects:
Splitting
GestaltMatcher
Methylation
Support vector machine
MN1
MCTT
Online Access:http://www.sciencedirect.com/science/article/pii/S2352396425001215
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