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- W3021522919 abstract "ABSTRACT BACKGROUND Clinical trials of single drugs for the treatment of Alzheimer Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially potent combinations. OBJECTIVE To use machine learning (ML) to extract the knowledge from two leading AD databases, and then use the machine to predict which combinations of the drugs in common between the two databases would be the most effective as treatments for AD. METHODS Three-layered neural networks (NNs) having compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants in either database, given the other data fields including age, demographic variables, comorbidities, and drugs taken. RESULTS The predictions from the separately trained NNs were strongly correlated. The best drug combinations, jointed determined from both sets of predictions, were high in NSAID, anticoagulant, lipid-lowering, and antihypertensive drugs, and female hormones. CONCLUSION The results suggest that AD, as a multifactorial disorder, could be effectively treated using a combination of repurposed drugs." @default.
- W3021522919 created "2020-05-13" @default.
- W3021522919 creator A5082504203 @default.
- W3021522919 date "2020-04-30" @default.
- W3021522919 modified "2023-10-14" @default.
- W3021522919 title "Predicting the potency of anti-Alzheimer drug combinations using machine learning" @default.
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- W3021522919 doi "https://doi.org/10.1101/2020.04.28.066340" @default.
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