AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease.
Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer's Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1009894 |
| _version_ | 1827999708078931968 |
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| author | Tamara Raschka Meemansa Sood Bruce Schultz Aybuge Altay Christian Ebeling Holger Fröhlich |
| author_facet | Tamara Raschka Meemansa Sood Bruce Schultz Aybuge Altay Christian Ebeling Holger Fröhlich |
| author_sort | Tamara Raschka |
| collection | DOAJ |
| description | Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer's Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders. This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease focused knowledge graph. Our approach, called iVAMBN, resulted in a quantitative model that allowed us to simulate a down-expression of the putative drug target CD33, including potential impact on cognitive impairment and brain pathophysiology. Experimental validation demonstrated a high overlap of molecular mechanism predicted to be altered by CD33 perturbation with cell line data. Altogether, our modeling approach may help to select promising drug targets. |
| first_indexed | 2024-04-10T06:03:54Z |
| format | Article |
| id | doaj.art-694483d60fd241c19ff4dc7fa87848d3 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-10T06:03:54Z |
| publishDate | 2023-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-694483d60fd241c19ff4dc7fa87848d32023-03-03T05:31:02ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-02-01192e100989410.1371/journal.pcbi.1009894AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease.Tamara RaschkaMeemansa SoodBruce SchultzAybuge AltayChristian EbelingHolger FröhlichModeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer's Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders. This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease focused knowledge graph. Our approach, called iVAMBN, resulted in a quantitative model that allowed us to simulate a down-expression of the putative drug target CD33, including potential impact on cognitive impairment and brain pathophysiology. Experimental validation demonstrated a high overlap of molecular mechanism predicted to be altered by CD33 perturbation with cell line data. Altogether, our modeling approach may help to select promising drug targets.https://doi.org/10.1371/journal.pcbi.1009894 |
| spellingShingle | Tamara Raschka Meemansa Sood Bruce Schultz Aybuge Altay Christian Ebeling Holger Fröhlich AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease. PLoS Computational Biology |
| title | AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease. |
| title_full | AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease. |
| title_fullStr | AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease. |
| title_full_unstemmed | AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease. |
| title_short | AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease. |
| title_sort | ai reveals insights into link between cd33 and cognitive impairment in alzheimer s disease |
| url | https://doi.org/10.1371/journal.pcbi.1009894 |
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