This paper presents a federated learning approach for imputing missing clinical assessments in Parkinson’s disease datasets, addressing privacy concerns while maintaining prediction accuracy across distributed healthcare institutions.
@article{reyes2025bridging,title={Bridging the Gaps: Imputation of Parkinson's Disease Clinical Assessments With Federated Learning},author={Reyes, J. and Noroozi, A. and Xiao, Y. and Kersten-Oertel, M.},journal={IEEE Journal of Biomedical and Health Informatics},year={2025},publisher={IEEE},doi={10.1109/JBHI.2025.3593459},}
2024
Sci Rep
ParaAntiProt provides paratope prediction using antibody and protein language models
M. Kalemati, A. Noroozi, A. Shahbakhsh, and 1 more author
ParaAntiProt provides paratope prediction using antibody and protein language models. This work presents a novel approach to predicting antibody paratopes using advanced protein language models, contributing to the field of computational immunology and antibody design.
@article{kalemati2024paraantiprot,title={ParaAntiProt provides paratope prediction using antibody and protein language models},author={Kalemati, M. and Noroozi, A. and Shahbakhsh, A. and others},journal={Scientific Reports},volume={14},pages={29141},year={2024},publisher={Nature Publishing Group},doi={10.1038/s41598-024-80940-y},url={https://doi.org/10.1038/s41598-024-80940-y},}