Apr 23, 2020

Bayesian neural networks for the optimisation of biological clocks in humans

BioRxiv : the Preprint Server for Biology
G. Alfonso, Juan R Gonzalez

Abstract

DNA methylation is related to aging. Some researchers, such as Horvath or Levine have managed to create epigenetic and biological clocks that predict chronological age using methylation data. These authors used Elastic Net methodology to build age predictors that had a high prediction accuracy. In this article, we propose to improve their performance by incorporating an additional step using neural networks trained with Bayesian learning. We shown that this approach outperforms the results obtained when using Horvath's method, neural networks directly, or when using other training algorithms, such as Levenberg-Marquardt's algorithm. The R-squared value obtained when using our proposed approach in empirical (out-of sample) data was 0.934, compared to 0.914 when using a different training algorithm (Levenberg Marquard), or 0.910 when applying the neural network directly (e.g. without first reducing the dimensionality of the data). The results were also tested in independent datasets that were not used during the training phase. Our method obtained better R-squared values and RMSE than Horvath's and Levine's method in 8 independent datasets. We demonstrate that building an age predictor using a Bayesian based algorithm provides ac...Continue Reading

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Mentioned in this Paper

Circo-Maren
Cell Line, Tumor
Genomics
Genome

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