Alexander A. Aksenov

This paper introduces molecular community networking to organize metabolite data, revealing hidden connections and discovering novel dipeptide-conjugated bile acids.

This paper introduces FactorIV, a causal inference method for high-dimensional multi-omics data, demonstrating its ability to recover causal effects in biological systems.

This paper describes a protocol using MetVAE to construct molecular networks from metabolomics data, identifying lipid correlations in a mouse model.

The paper introduces SWipe, a low-cost stool collection method for gut metabolomics that enables accurate, reproducible, and convenient sample collection for large-scale studies.

This study uses spatial metabolomics to identify bactericidal compounds in citrus plants infected with Candidatus Liberibacter asiaticus, suggesting potential alternatives to antibiotics for HLB treatment.

S-Wipe is a low-cost stool collection method for metabolomic gut health tracking that enables reproducible, stable, and convenient sample collection for diagnostic and longitudinal studies.

The paper creates a suspect spectral library for untargeted metabolomics by propagating annotations from public MS/MS spectra to improve interpretation of unannotated spectra.

The paper introduces Ion Identity Molecular Networking to connect ion species of the same molecule, improving network connectivity and annotation.

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.

The paper introduces FBMN, a method for improving molecular networking by enhancing quantitative analysis and isomer distinction using feature detection and alignment tools in the GNPS platform.

The paper develops ReDU, a tool for discovering and re-analyzing metabolomics data in GNPS, following FAIR principles.

The paper compares correlation/proportionality methods with MMvec for identifying microbe-metabolite interactions and finds MMvec performs better on real data due to its handling of sparsity.

MASST enables querying of public small-molecule tandem MS data to discover molecular relationships in metabolomics and natural product datasets.

This paper uses untargeted mass spectrometry to analyze chemical changes in foods during preparation and storage, finding that some processes alter molecular composition while others primarily involve extraction.