Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics

arXiv:2310.07990v2 Announce Type: replace-cross
Abstract: Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder …

abstract accuracy arxiv autoencoder challenge cs.ir cs.lg data genome imputation integration q-bio.gn sequencing stat.ap studies type value view