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

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 to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omi

SUBMITTER: Zhao C 

PROVIDER: S-EPMC10593076 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature