Artificial intelligence-driven genotype-epigenotype-phenotype approaches to resolve challenges in syndrome diagnostics.

Fiche publication

Date publication

avril 2025

Journal

EBioMedicine

Auteurs

Membres identifiés du Cancéropôle Est :
Pr FAIVRE Laurence

Tous les auteurs :
Mak CCY, Klinkhammer H, Choufani S, Reko N, Christman AK, Pisan E, Chui MMC, Lee M, Leduc F, Dempsey JC, Sanchez-Lara PA, Bombei HM, Bernat JA, Faivre L, Mau-Them FT, Palafoll IV, Canham N, Sarkar A, Zarate YA, Callewaert B, Bukowska-Olech E, Jamsheer A, Zankl A, Willems M, Duncan L, Isidor B, Cogne B, Boute O, Vanlerberghe C, Goldenberg A, Stolerman E, Low KJ, Gilard V, Amiel J, Lin AE, Gordon CT, Doherty D, Krawitz PM, Weksberg R, Hsieh TC, Chung BHY

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/40280028

Résumé

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-generation phenotyping (NGP) and DNA methylation (DNAm) can be utilized to expedite syndrome delineation within a single gene.