Sirota, Marina

Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++

Davydov, E. V., Goode, D. L., Sirota, M., Cooper, G. M., Sidow, A., & Batzoglou, S. (2010) · PLoS Computational Biology

Genomics and Phylogenetic StudiesGenomics and Chromatin Dynamics

Cited by 1,757

Expression-based genome-wide association study links the receptor CD44 in adipose tissue with type 2 diabetes

Kodama, K., Horikoshi, M., Toda, K., Yamada, S., Hara, K., Irie, J., Sirota, M., Morgan, A. A., Chen, R., Ohtsu, H., et al. (2012) · Proceedings of the National Academy of Sciences

Adipokines, Inflammation, and Metabolic DiseasesGenetic Associations and Epidemiology

Cited by 171

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

Harnessing EHR data for health research

Tang, A. S., Woldemariam, S. R., Miramontes, S., Norgeot, B., Oskotsky, T. T., & Sirota, M. (2024) · Nature Medicine

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 87

Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights

Tang, A. S., Rankin, K. P., Cerono, G., Miramontes, S., Mills, H., Roger, J., Zeng, B., Nelson, C., Soman, K., Woldemariam, S., et al. (2024) · Nature Aging

Dementia and Cognitive Impairment ResearchMachine Learning in Healthcare

Cited by 86

Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women With Alzheimer’s Disease

Paranjpe, M. D., Belonwu, S., Wang, J. K., Oskotsky, T., Gupta, A., Taubes, A., Zalocusky, K. A., Paranjpe, I., Glicksberg, B. S., Huang, Y., et al. (2021) · Frontiers in Aging Neuroscience

Alzheimer's disease research and treatmentsNeuroinflammation and Neurodegeneration Mechanisms

Cited by 49

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

Multi-Modal Single-Cell Sequencing Identifies Cellular Immunophenotypes Associated With Juvenile Dermatomyositis Disease Activity

Neely, J., Hartoularos, G., Bunis, D., Sun, Y., Lee, D., Kim, S., Ye, C. J., & Sirota, M. (2022) · Frontiers in Immunology

Inflammatory Myopathies and DermatomyositisSystemic Sclerosis and Related Diseases

Cited by 26

Real world perspectives on endometriosis disease phenotyping through surgery, omics, health data, and artificial intelligence

Nezhat, C. R., Oskotsky, T. T., Robinson, J. F., Fisher, S. J., Tsuei, A., Liu, B., Irwin, J. C., Gaudilliere, B., Sirota, M., Stevenson, D. K., et al. (2025) · npj Women s Health

Endometriosis Research and TreatmentEndometrial and Cervical Cancer Treatments

Cited by 25

Artificial intelligence–enabled precision medicine for inflammatory skin diseases

Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026) · Journal of Investigative Dermatology

Artificial Intelligence in Healthcare and EducationDermatology and Skin Diseases

Cited by 10

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning

Zhou, M., Tang, A. S., Zhang, H., Xu, Z., Ke, A. M. C., Su, C., Huang, Y., Mantyh, W. G., Jaffee, M. S., Rankin, K. P., et al. (2025) · Journal of Biomedical Informatics

Dementia and Cognitive Impairment ResearchMachine Learning in Healthcare

Cited by 9