Bonakdarpour, Borna

Predicting alzheimer’s disease progression using multi-modal deep learning approach

Lee, G., Nho, K., Kang, B., Sohn, K.-A., Kim, D., Initiative, F. A. D. N., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., et al. (2019) · Scientific Reports

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 406

Alzheimer disease brain atrophy subtypes are associated with cognition and rate of decline

Risacher, S. L., Anderson, W. H., Charil, A., Castelluccio, P. F., Shcherbinin, S., Saykin, A. J., Schwarz, A. J., Initiative, F. T. A. D. N., Initiative, A. D. N., Weiner, M. W., et al. (2017) · Neurology

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 163

Identification of genetic risk factors in the Chinese population implicates a role of immune system in Alzheimer’s disease pathogenesis

Zhou, X., Chen, Y., Mok, K. Y., Zhao, Q., Chen, K., Chen, Y., Hardy, J., Li, Y., Fu, A. K. Y., Guo, Q., et al. (2018) · Proceedings of the National Academy of Sciences

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 96

Staging tau pathology with tau PET in Alzheimer’s disease: a longitudinal study

Chen, S.-D., Lu, J.-Y., Li, H.-Q., Yang, Y.-X., Jiang, J.-H., Cui, M., Zuo, C.-T., Tan, L., Dong, Q., Yu, J.-T., et al. (2021) · Translational Psychiatry

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 71

AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease

Mao, C., Xu, J., Rasmussen, L., Li, Y., Adekkanattu, P., Pacheco, J., Bonakdarpour, B., Vassar, R., Shen, L., Jiang, G., et al. (2023) · Journal of Biomedical Informatics

Machine Learning in HealthcareDementia and Cognitive Impairment Research

Cited by 67

Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease

Huang, M., Yang, W., Feng, Q., Chen, W., Initiative, T. A. D. N., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., Jagust, W., et al. (2017) · Scientific Reports

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 55

Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification

Hett, K., Ta, V.-T., Catheline, G., Tourdias, T., Manjón, J. V., Coupé, P., Initiative, A. D. N., Weiner, M. W., Aisen, P., Petersen, R., et al. (2019) · Scientific Reports

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 49

Genetic studies of plasma analytes identify novel potential biomarkers for several complex traits

Deming, Y., Xia, J., Cai, Y., Lord, J., Del-Aguila, J. L., Fernandez, M. V., Carrell, D., Black, K., Budde, J., Ma, S., et al. (2016) · Scientific Reports

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 28

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Jo, T., Kim, J., Bice, P., Huynh, K., Wang, T., Arnold, M., Meikle, P. J., Giles, C., Kaddurah-Daouk, R., Saykin, A. J., et al. (2023) · eBioMedicine

Dementia and Cognitive Impairment ResearchMetabolomics and Mass Spectrometry Studies

Cited by 15