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bioRxiv · 10.1101/2025.05.29.656750

10 Years of Variational Autoencoder: Insights from Cancer Temporal Progression Studies, a Systematic Literature Review

Prol-Castelo, G.·Cirillo, D.·Valencia, A.
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Abstract

Deep learning methods, including deep representation learning (DRL) approaches such as variational au-toencoders (VAEs), have been widely applied to cancer omics data to address the high dimensionality of these datasets. Despite remarkable advances, cancer remains a complex and dynamic disease that is challenging to study, and the temporal resolution of cancer progression captured by omics-based studies remains limited. In this systematic literature review, we explore the use of DRL, particularly the VAE, in cancer omics studies for modeling time-related processes, such as tumor progression and evolutionary dynamics. Our work reveals that these methods most commonly support subtyping, diagnosis, and prognosis in this context, but rarely emphasize temporal information. We observed that the scarcity of longitudinal omics data currently limits deeper temporal analyses that could enhance these applications. We propose that applying the VAE as a generative model to study cancer in time, for example, focusing on cancer staging, could lead to meaningful advancements in our understanding of the disease. Biographical NoteO_LIGuillermo Prol-Castelo is a PhD student at the Barcelona Supercomputing Center and Universitat Pompeu Fabra, where he works on the application of deep learning methods to cancer studies. C_LIO_LIDavide Cirillo is the head of the Machine Learning for Biomedical Research Unit at the Barcelona Supercomputing Center. He is an expert in predictive modeling for Precision Medicine using Network Biology and Machine Learning. C_LIO_LIAlfonso Valencia is the principal investigator of the Computational Biology Group at the Barcelona Supercomputing Center. He is a leading expert in protein coevolution, disease networks and modelling cellular systems. C_LIO_LIThe Barcelona Supercomputing Center is a public research center that provides high-performance computing infrastructure to support scientific research in a wide range of fields, including life sciences. C_LI Key PointsO_LIThere is a growing interest on the application of deep learning methods, such as Deep Representation Learning (DRL), to cancer studies. C_LIO_LICancer is a complex and dynamic disease, whose temporal dynamics are not yet fully captured in omics-based studies. C_LIO_LImong DRL methods, the Variational Autoencoder (VAE) using omics-based data has been widely used in cancer studies, particularly for subtyping, diagnosis, and prognosis. C_LIO_LIThe temporal aspects of cancer progression are often insufficiently captured in omics-based studies, primarily due to the scarcity of longitudinal data. C_LIO_LIApplying the VAE as a generative model to study cancer in time, such as focusing on cancer staging, could lead to significant advancements in our understanding of cancer. C_LI

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Prol-Castelo, G., Cirillo, D., Valencia, A.. 2025-06-05. 10 Years of Variational Autoencoder: Insights from Cancer Temporal Progression Studies, a Systematic Literature Review. https://doi.org/10.1101/2025.05.29.656750

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