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Poster
in
Workshop: 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH)

Longitudinal Variational Autoencoder for Compositional Data Analysis

Mine Öğretir · Harri Lähdesmäki · Jamie Norton

Keywords: [ longitudinal data analysis ] [ Deep Generative Models ] [ variational autoencoders ] [ Gaussian process ]


Abstract:

The analysis of compositional longitudinal data, particularly in microbiome time-series, is a challenging task due to its high-dimensional, sparse, and compositional nature. In this paper, we introduce a novel Gaussian process (GP) prior variational autoencoder for longitudinal data analysis with a multinomial likelihood (MNLVAE) that is specifically designed for compositional time-series analysis. Our generative deep learning model captures complex interactions among microbial taxa while accounting for the compositional structure of the data. We utilize centered log-ratio (CLR) and isometric log-ratio (ILR) transformations to preprocess and transform compositional count data, and utilize a latent multi-output additive GP model to enable prediction of future observations. Our experiments demonstrate that MNLVAE outperforms competing method, offering improved prediction performance across different longitudinal microbiome datasets.

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