{"doi":"10.1101/2021.01.14.426721","title":"Variational autoencoders learn universal latent representations of metabolomics data","abstract":"Abstract Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. Here, we trained a VAE on a large-scale metabolomics population cohort of human blood samples consisting of over 4,500 individuals. We analyzed the pathway composition of the latent space using a global feature importance score, which showed that latent dimensions represent distinct cellular processes. To demonstrate model generalizability, we generated latent representations of unseen metabolomics datasets on type 2 diabetes, schizophrenia, and acute myeloid leukemia and found significant correlations with clinical patient groups. Taken together, we demonstrate for the first time that the VAE is a powerful method that learns biologically meaningful, nonlinear, and universal latent representations of metabolomics data.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":218321,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9488,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":816121,"name":"Annalise Schweickart","orcid":"0000-0001-9691-3741","position":1,"is_corresponding":false},{"id":237480,"name":"Leandro Cerchietti","orcid":"0000-0003-0608-1350","position":2,"is_corresponding":false},{"id":241452,"name":"Elisabeth Paietta","orcid":"0000-0002-0561-5544","position":3,"is_corresponding":false},{"id":489811,"name":"Hugo F. Fernández","orcid":"0000-0002-7322-0392","position":4,"is_corresponding":false},{"id":816122,"name":"Hassen Al‐Amin","orcid":"0000-0001-6358-1541","position":5,"is_corresponding":false},{"id":2350,"name":"Karsten Suhre","orcid":"0000-0001-9638-3912","position":6,"is_corresponding":false},{"id":2337,"name":"Jan Krumsiek","orcid":"0000-0003-4734-3791","position":7,"is_corresponding":false},{"id":816562,"name":"Daniel P. Gomari","orcid":null,"position":0,"is_corresponding":true}],"reference_count":62,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:53:29.626149Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}