Fatemeh Omidi

Contributed to the EvoPronet project, leveraging DNA and protein network interactions to design bio-inspired computational frameworks with AI-based approaches. Applied transformer-based architectures to analyze large-scale protein interaction networks, improving predictive accuracy and interpretability. Collaborated with an interdisciplinary team to integrate computational AI methods with biologic

Spearheaded AI-based initiatives to improve diagnostic yield for rare diseases, successfully identifying causative variants in 86 out of 95 clinical cases. Developed a scalable data management system using MongoDB and Python to store and annotate over 8 million genomic variants, optimizing the system for efficient reanalysis. Designed and implemented end-to-end workflows for clinical genomic pipel

Integrated additional layers to enhance the precision and adaptability of existing Machine Learning workflows. Worked under the supervision of Dr. Marta Mele to improve computational models.

Managed and analyzed Whole Exome Sequencing (WES) data for clinical diagnostics, ensuring data quality and integrity. Collaborated closely with clinical teams to integrate genetic data with phenotypic and familial histories, enhancing diagnostic accuracy for patients.

Pursuing a Ph.D. in Biomedicine with a focus on improving diagnostic yield and time-to-diagnosis for rare diseases. Developing AI-based algorithms for this purpose. Thesis title: Improving diagnostic yield and time-to-diagnosis of rare diseases by developing AI-based algorithms.

Grade: 18.4/20 (4/4)

Completed a Master of Science in Medical Genetics. Investigated genetic variants through network interactions to design bio-inspired computational frameworks, with a focus on AI-based approaches in patients with congenital cataract. Thesis title: Investigation of genetic variants through network interactions to design bio-inspired computational frameworks with a focus on AI-based approaches in pat

Grade: 17.64/20 (4/4)

Earned a Bachelor of Science in Molecular Genetics. Graduated in the top 5% of the class.