Integrated Multi-Omics Approach to Predict Dementia: Using an Explainable Variational Autoencoder (E-Vae) Classifier Model

No Thumbnail Available

Persistent link to this item

View Statistics

Journal Title

Journal ISSN

Volume Title


Integrated Multi-Omics Approach to Predict Dementia: Using an Explainable Variational Autoencoder (E-Vae) Classifier Model

Published Date




Thesis or Dissertation


Alzheimer’s disease (AD) and AD-related dementias (ADRD) are complex multifactorial processes where epigenetic and biochemical changes occur many years before the onset of clinical symptoms. During the last decade, large amounts of high-throughput molecular data including genetic variants, and epigenetic and transcriptomic data from blood and brain tissues have improved our understanding of complex molecular mechanisms associated with pathways of AD/ADRD. The application of deep learning methods to analyze integrated multi-omics data may be a powerful approach to elucidate the biological mechanisms in AD. This dissertation aims to develop a framework to process high-dimensional genomics data and to integrate multi-omics data to classify dementia utilizing an end-to-end deep learning classifier model. We developed an end-to-end deep learning explainable variational autoencoder (E-VAE) classifier model, using genome-wide genetic variants (GWAS SNPs) with an accuracy = 0.71 and sensitivity = 0.73 (Chapter 2), and transcriptome (RNA-Seq) with an accuracy = 0.83 and sensitivity = 0.77 (Chapter 3) and epigenetic (DNA methylation) with an accuracy = 0.79 and sensitivity = 0.88 (Chapter 4) collected from 2700 study participants in the Health and Retirement Study (HRS). We utilized a framework to integrate genetic variants and RNAseq data and developed a multi-omics (GWAS SNPs + RNAseq) explainable variational autoencoder (E-VAE) classifier model to predict dementia (Chapter 5) with an accuracy = 0.73 and sensitivity = 0.73. We evaluated the generalizability of the E-VAE classifier models in an external dataset from Religious Orders Study/Memory and Aging Project (ROSMAP) and the multi-omics E-VAE classifier model achieved an accuracy = 0.67 and sensitivity = 0.77. We found that the integrated multi-omics E-VAE classifier model achieved better generalizability in the external data compared to a penalized logistic regression model (accuracy = 0.73 and sensitivity = 0.33) trained using GWAS SNPs and RNAseq. Utilizing the linear decoder in the E-VAE classifier model, we extracted biological interpretable latent features and translated the top-weighted genes into biological insights. We identified genes known to be involved in the pathogenesis of AD/ADRD and novel genes that were not studied previously in association with AD/ADRD. In summary, this dissertation demonstrates the utility of deep learning methods to analyze complex multi-omics data to classify AD/ADRD. The explainable deep learning model, allowed us to interpret the biological importance of deep representations of multi-omics features by optimizing a classifier model for dementia and generating new hypotheses to advance our understanding of the pathobiology of AD/ADRD.



University of Minnesota Ph.D. dissertation. April 2023. Major: Computer Science. Advisors: Bharat Thyagarajan, Weihua Guan. 1 computer file (PDF); xi, 97 pages.

Related to




Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Vivek, Sithara. (2023). Integrated Multi-Omics Approach to Predict Dementia: Using an Explainable Variational Autoencoder (E-Vae) Classifier Model. Retrieved from the University Digital Conservancy,

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.