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A single-cell transcriptomic analysis of endometriosis, endometriomas, eutopic endometrial samples and uninvolved ovary tissues highlights cell populations characteristic of these tissue types. Transcriptional and cellular heterogeneity across tissues suggests novel therapeutic targets and biomarkers for this disease.
An AI-based decision support system for ovarian cancer diagnosis, based on our research leveraging ML and Explainable AI. The system predicts cancer risk and explains decisions to aid healthcare professionals.
The automation pipeline processes mutation and clinical data, aligning and cleaning the inputs before performing a detailed analysis. It enables visualization and interpretation of mutation signatures with a focus on specific genes and cancer subtypes.