Frohock, Z. | Search bioRxiv

bioRxiv2025

Constructing a consensus serum metabolome

Blood analysis is the most common in biomedical applications and a reference metabolome will be critical for effective annotation and for guiding scientific investigations. However, compiling such a reference is hindered by many technical challenges, despite the availability of large amount of metabolomics data today. Based on a new set of data structures and tools, we have assembled a consensus serum metabolome (CSM) from over 100,000 mass spectrometry acquisitions of more than 200 million spectra. This provides a comprehensive survey of human blood chemistry, revealing the frequency dependent nature of metabolome and exposome. Major gaps are found between CSM and the current databases. The CSM enables community-level data alignment and significantly improves annotation quality of LC-MS metabolomics. HighlightsO_LIA reference of human biochemistry linked to observation frequency C_LIO_LIMajor gaps revealed in current databases and experimental methods C_LIO_LIEnabling cross-laboratory, cross-platform data alignment C_LIO_LIAccelerated and cumulative metabolite annotation C_LI

biochemistry↗

bioRxiv2024

An explainable graph neural network approach for integrating multi-omics data with prior knowledge to identify biomarkers from interacting biological domains.

The rapid growth of multi-omics datasets, in addition to the wealth of existing biological prior knowledge, necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying disease-related molecular markers. We propose a framework for supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework leverages graph neural networks (GNNs) to model the relationships among features from high-dimensional omics data and set transformers to integrate low-dimensional representations of omics features. Furthermore, our framework incorporates explainability methods to elucidate important biomarkers and extract interaction relationships between biological quantities of interest. We demonstrate the effectiveness of our approach by applying it to Alzheimers disease (AD) multi-omics data from the ROSMAP cohort, showing that the integration of transcriptomics and proteomics data with AD biological domain network priors improves the prediction accuracy of AD status and highlights functional AD biomarkers.

bioinformatics↗