Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice.
Andres Terre, Helena
Frontiers in genetics
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Simidjievski, N., Bodnar, C., Tariq, I., Scherer, P., Andres Terre, H., Shams, Z., Jamnik, M., & et al. (2019). Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice.. Frontiers in genetics, 10 https://doi.org/10.3389/fgene.2019.01205
International initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyze such data, several machine learning, bioinformatics, and statistical methods have been applied, among them neural networks such as autoencoders. Although these models provide a good statistical learning framework to analyze multi-omic and/or clinical data, there is a distinct lack of work on how to integrate diverse patient data and identify the optimal design best suited to the available data.In this paper, we investigate several autoencoder architectures that integrate a variety of cancer patient data types (e.g., multi-omics and clinical data). We perform extensive analyses of these approaches and provide a clear methodological and computational framework for designing systems that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these networks can be designed, built, and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data. The results show that these approaches yield relevant data representations that, in turn, lead to accurate and stable diagnosis.
Artificial intelligence, Machine Learning, Deep Learning, Multi-omic Analysis, Variational Autoencoder, Bioinformactics, Cancer–breast Cancer, Integrative Data Analyses
External DOI: https://doi.org/10.3389/fgene.2019.01205
This record's URL: https://www.repository.cam.ac.uk/handle/1810/301968
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/