Variational autoencoders learn universal latent representations of metabolomics data is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2021). On theSindex it has a DataRank of 0.165. It has been cited 2 times.
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Base Score Contribution
0.165
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Institutes of Health
Grant: 5U19AG063744-02
Alzheimer's Gut Microbiome Project
National Institutes of Health
Grant: 5UG1CA189859-03
Minority-Based Community Oncology Research Program
Wellcome Trust
Grant: unidentified
unidentified
National Institutes of Health
Grant: 3U10CA180820-02S3
ECOG-ACRIN Operations Center
Fields of Study
Keywords
Sustainable Development Goals
Additional file 1 of Integrated proteomic and metabolomic modules identified as biomarkers of mortality in the Atherosclerosis Risk in Communities study and the African American Study of Kidney Disease and Hypertension
Additional file 1 of Integrated proteomic and metabolomic modules identified as biomarkers of mortality in the Atherosclerosis Risk in Communities study and the African American Study of Kidney Disease and Hypertension