Multi-task deep autoencoder to predict Alzheimer's disease progression using temporal DNA methylation data in peripheral blood

Abstract

MotivationTraditional approaches for diagnosing Alzheimers disease (AD) such as brain imaging and cerebrospinal fluid are invasive and expensive. It is desirable to develop a useful diagnostic tool by exploiting biomarkers obtained from peripheral tissues due to their noninvasive and easily accessible characteristics. However, the capacity of using DNA methylation data in peripheral blood for predicting AD progression is rarely known. It is also challenging to develop an efficient prediction model considering the complex and high-dimensional DNA methylation data in a longitudinal study. ResultsWe develop two multi-task deep autoencoders, which are based on convolutional autoencoder and long short-term memory autoencoder to learn the compressed feature representations by jointly minimizing the reconstruction error and maximizing the prediction accuracy. By benchmarking on longitudinal methylation data collected from peripheral blood in Alzheimers Disease Neuroimaging Initiative, we demonstrate that the multi-task deep autoencoders outperform state-of-the-art machine learning approaches for both predicting AD progression and reconstructing the temporal methylation profiles. In addition, the proposed multi-task deep autoencoders can predict AD progression accurately using only historical data and the performance is further improved by including all temporal data. Availabilityhttps://github.com/lichen-lab/MTAE