Study combines biomimetic AI, digital twins and multiomics to unveil potential genetic drivers of endometriosis

The study developed a biomimetic digital twin ecosystem that tailors analysis to endometriosis research. This system integrates expert knowledge and raw clinical data to improve identification and analysis of molecular profiles linked to the disease. Image licensed under Creative Commons from The Journal of Molecular Diagnostics., Figure 2.

Endometriosis, a condition where endometrial tissue grows outside the uterus, has a strong genetic underpinning. A new study published in the Journal of Molecular Diagnostics sheds light on this connection. Researchers from a team including Genzeva, LumaGene, RYLTI Biopharma, Brigham & Women’s Hospital of Harvard University and QIAGEN Digital Insights used a unique approach in their analysis of endometriosis patient samples. They combined multiple data sources, spanning multiomics data, next-generation sequencing, phenot…

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