Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer’s disease

Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer’s disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-cod...

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Detalles Bibliográficos
Autores principales: Xu, Jielin, Mao, Chengsheng, Hou, Yuan, Luo, Yuan, Binder, Jessica L., Zhou, Yadi, Bekris, Lynn M., Shin, Jiyoung, Hu, Ming, Wang, Fei, Eng, Charis, Oprea, Tudor I., Flanagan, Margaret E., Pieper, Andrew A., Cummings, Jeffrey, Leverenz, James B., Cheng, Feixiong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837836/
https://www.ncbi.nlm.nih.gov/pubmed/36450252
http://dx.doi.org/10.1016/j.celrep.2022.111717

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837836/
https://www.ncbi.nlm.nih.gov/pubmed/36450252
http://dx.doi.org/10.1016/j.celrep.2022.111717

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