Xiaotao Shen

This study identifies Roseburia hominis-derived 10-hydroxystearic acid as a metabolite that improves metabolic health by restoring hepatic fatty acid metabolism in mice.

FeatureMAP improves interpretation of untargeted metabolomics data by integrating annotation, network refinement, and functional analysis, revealing metabolic patterns in Alzheimer's disease.

LagCI identifies temporal causal relationships in dense multi-omics time series by analyzing lag-correlation profiles and filtering spurious associations.

This paper shows that LOESS and DE-SWAN can create false 'waves' of molecular aging in data, challenging their statistical validity.

FeatureMSEA enables metabolite set enrichment analysis from ambiguous LC-MS metabolomics features, identifying dysregulated pathways in Huntington's disease.

MetaNet is an R package for scalable multi-omics network analysis, offering faster computation and better memory efficiency than existing tools, with applications in microbial and exposome-transcriptome studies.

eBiota designs microbial communities from large strain pools to generate target products using optimization and deep learning, showing accurate predictions and experimental validation.

The paper examines how diet affects gut microbiome and plasma metabolome across insulin resistance states, finding stronger diet-omics associations in insulin-sensitive individuals.

TidyMass2 improves untargeted metabolomics by inferring metabolite origins and analyzing functional modules, increasing biologically interpretable features from 5.8% to 58.8% in pregnancy urine data.

MAPA uses large language models to reduce redundancy in pathway analysis and improve biological interpretation of multi-omics data.

Prophet is a transformer-based framework that improves disease diagnosis and prediction using proteomics data from the UK Biobank, achieving high accuracy and identifying new protein-disease associations.

Kun-peng is a metagenomic classifier that outperforms existing tools in speed, memory efficiency, and accuracy for large-scale pan-domain classification.

This study tracks microbiome changes across four body sites in 86 participants over six years, revealing site-specific stability, individualized dynamics, and altered patterns in insulin-resistant individuals.

The paper introduces GRSA, a generalized reporter score-based method for multi-group and longitudinal omics data, showing increased sensitivity and applied it to microbiome, transcriptome, and metabolome data.

The paper introduces microbiomedataset, an R package based on the tidyverse framework, to standardize and streamline microbiome data processing and analysis.

This study maps the organism-wide secretome response to exercise in mice, identifying 200+ exercise-regulated protein pairs and uncovering anti-obesity and anti-diabetic roles of a liver-secreted proteoform.

The paper introduces deepPseudoMSI, a deep learning framework that converts LC-MS data into pseudo-MS images for improved disease diagnosis in precision medicine.

massDatabase provides an R package for streamlined compound annotation and pathway enrichment analysis by integrating public compound and pathway databases.

tidyMass provides a reproducible and shareable framework for LC-MS metabolomics data analysis.

The paper uses longitudinal urine metabolomics to predict gestational age with a machine-learning model, showing non-invasive pregnancy dating potential.

This study combines common and rare variant analyses with single-cell multiomics to identify cell-type-specific genetic risk factors for severe COVID-19, revealing molecular mechanisms and potential therapeutic targets.

This study integrates longitudinal personal exposome and multi-omics data to show dynamic interactions between environmental exposures and internal biomolecules, highlighting key agrochemicals and fungi.

metID automates compound annotation for LC-MS data by integrating multiple databases, improving efficiency and reproducibility.

This study integrates metabolomics and proteomics data from NGLY1 deficiency patient samples and cellular models to identify molecular phenotypes linked to immune activation and metabolic disturbances.

The paper introduces a recursive algorithm and webserver, MetDNA, for large-scale and unambiguous metabolite identification in untargeted metabolomics.