Bicak, Mesude

Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., Freitas, J. K. D., Wanyan, T., et al. (2020) · JMIR Medical Informatics

Machine Learning in HealthcarePrivacy-Preserving Technologies in Data

Cited by 253

Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer’s disease

Taubes, A., Nova, P., Zalocusky, K. A., Kosti, I., Bicak, M., Zilberter, M. Y., Hao, Y., Yoon, S. Y., Oskotsky, T., Pineda, S., et al. (2021) · Nature Aging

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 118

Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations

Tang, A. S., Oskotsky, T., Havaldar, S., Mantyh, W. G., Bicak, M., Solsberg, C. W., Woldemariam, S., Zeng, B., Hu, Z., Oskotsky, B., et al. (2022) · Nature Communications

Dementia and Cognitive Impairment ResearchMachine Learning in Healthcare

Cited by 45

PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting

Saxena, M., Marron, T. U., Kodysh, J., Finnigan, J. P., Onkar, S., Kaminska, A., Tuballes, K., Guo, R., Sabado, R. L., Meseck, M., et al. (2025) · Cancer Discovery

Immunotherapy and Immune ResponsesCancer Immunotherapy and Biomarkers

Cited by 28