Xiaotao Shen

Gut Roseburia hominis-derived 10-hydroxystearic acid alleviates metabolic disorder by restoring hepatic fatty acid metabolism

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 enables network-guided interpretation of untargeted metabolomics

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

LagCI Enables Inference of Temporal Causal Relationships from Dense Multi-Omic Time Series

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

LOESS and DE-SWAN can induce artifactual "waves" of molecular aging

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

FeatureMSEA: Metabolic Feature-based Metabolite Set Enrichment Analysis

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

MetaNet: a scalable and integrated tool for reproducible omics network analysis

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: Designing microbial communities from large seed pools with desired function using rapid optimization and deep learning

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

Insulin resistance modifies longitudinal multi-omics responses to habitual diet

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: Advancing LC-MS Untargeted Metabolomics Through Metabolite Origin Inference and Metabolic Feature-based Functional Module Analysis

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.

Leveraging Large Language Models for Redundancy-Aware Pathway Analysis and Deep Biological Interpretation

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

A multistage, multitask transformer-based framework for multi-disease diagnosis and prediction using personal proteomes

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: an ultra-memory-efficient, fast, and accurate pan-domain taxonomic classifier for all

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

Generalized Reporter Score-based Enrichment Analysis for Omics Data

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.

microbiomedataset: A tidyverse-style framework for organizing and processing microbiome data

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

Organism-wide secretome mapping uncovers pathways of tissue crosstalk in exercise

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.

Deep Learning-based Pseudo-Mass Spectrometry Imaging Analysis for Precision Medicine

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: utilities for the operation of the public compound and pathway database

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

TidyMass: An Object-oriented Reproducible Analysis Framework for LC-MS Data

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

Longitudinal Urine Metabolic Profiling and Gestational Age Prediction in Pregnancy

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

Common and rare variant analyses combined with single-cell multiomics reveal cell-type-specific molecular mechanisms of COVID-19 severity

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.

Precision environmental health monitoring by longitudinal exposome and multi-omics profiling

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: A R package for automatable compound annotation for LC-MS-based data

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

Integration and comparison of multi-omics profiles of NGLY1 deficiency plasma and cellular models to identify clinically relevant molecular phenotypes

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.

Metabolic Reaction Network-based Recursive Metabolite Identification for Untargeted Metabolomics

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