Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have provided largely negative results, so far. Here, the authors present a machine learning framework that quantifies potential associations between the pathology of AD severity and gene-based molecular mechanisms to enable drug rep...
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/93e763bd3055417ea9c559a7b6fa24d5 |
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| Sumario: | Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have provided largely negative results, so far. Here, the authors present a machine learning framework that quantifies potential associations between the pathology of AD severity and gene-based molecular mechanisms to enable drug repurposing. |
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