| 1 |
2023 |
Hind Alamro, Maha A. Thafar, Somayah Albaradei, Takashi Gojobori, Magbubah Essack, Xin Gao. Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets, Scientific Reports. 2023; 13: 4979.
https://doi.org/10.1038/s41598-023-30904-5 |
| 2 |
2021 |
Joseph Geraci, Shattering cancer with quantum machine learning: A preview, Patterns. 2021; 2: 100281100281.
https://doi.org/10.1016/j.patter.2021.100281 |
| 3 |
2021 |
Cook Moses, Qorri Bessi, Baskar Amruth, Ziauddin Jalal, Pani Luca, Yenkanchi Shashibushan, Joseph Geraci. 2021;
https://doi.org/10.1101/2021.07.27.21261075 |
| 4 |
2022 |
Alice S. Tang, Tomiko Oskotsky, Shreyas Havaldar, William G. Mantyh, Mesude Bicak, Caroline Warly Solsberg, Sarah Woldemariam, Billy Zeng, Zicheng Hu, Boris Oskotsky, Dena Dubal, Isabel E. Allen, Benjamin S. Glicksberg, Marina Sirota. Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations, Nature Communications. 2022; 13: 675.
https://doi.org/10.1038/s41467-022-28273-0 |
| 5 |
2024 |
Zhihao Zhang, Xiangtao Liu, Suixia Zhang, Zhixin Song, Ke Lu, Wenzhong Yang. A review and analysis of key biomarkers in Alzheimer’s disease, Frontiers in Neuroscience. 2024; 18: 1358998.
https://doi.org/10.3389/fnins.2024.1358998 |
| 6 |
2024 |
Joseph Geraci, Ravi Bhargava, Bessi Qorri, Paul Leonchyk, Douglas Cook, Moses Cook, Fanny Sie, Luca Pani. Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS, Frontiers in Computational Neuroscience. 2024; 17: 1199736.
https://doi.org/10.3389/fncom.2023.1199736 |
