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Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data

2017/08/23 by Paolo Inglese, Inglese, Paolo, James L. Alexander +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #FOS: Biological sciences #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Metabolomics and Mass Spectrometry Studies #Quantitative Methods (q-bio.QM) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1708.07012

openalex publication_date 2017/08/23 · openalex created_date 2017/08/31 · openalex updated_date 2026/07/28

Abstract

The paper presents the application of Variational Autoencoders (VAE) for data dimensionality reduction and explorative analysis of mass spectrometry imaging data (MSI). The results confirm that VAEs are capable of detecting the patterns associated with the different tissue sub-types with performance than standard approaches.

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