Qiwei Li

DeMoP uses a language-model-guided mixture-of-experts framework to improve cancer prognosis by integrating clinical and molecular data across cohorts.

spaGSE improves pathway enrichment analysis in spatial transcriptomics by incorporating spatial dependence, identifying biologically relevant pathways in cancer and developmental tissues.

This study introduces ReMiTT, a computational method to identify T cell migration trails in spatial transcriptomics data, revealing chemokine trends and enriched pathways associated with T cell movement in tumors.

SpaFun identifies domain-representative genes in spatial transcriptomics data using functional principal component analysis, outperforming existing methods in accuracy and efficiency.

This paper introduces Spa3D, a method that reconstructs 3D spatial structures from 2D spatial transcriptomics data, improving spatial domain identification, cell-cell communication analysis, and developmental trajectory modeling.

This study shows paclitaxel reduces pulmonary arterial hypertension by inhibiting M2a macrophage polarization through the NF-κB pathway in a rat model.

This paper introduces iMOSCATO, a Bayesian model that integrates single-cell RNA-seq and spatial transcriptomics data to decompose cell-type compositions and detect spatial domains.

This study introduces a Bayesian Dirichlet-multinomial regression model to integrate single-cell RNA sequencing data with clinical variables, uncovering cell-type-specific associations across three diseases.

BayeSMART improves multi-sample spatial clustering by integrating AI-reconstructed single-cell data with spatial context, outperforming existing methods in accuracy and efficiency.

The paper introduces a Bayesian clustering method with feature selection for spatially resolved transcriptomics data, improving accuracy through a zero-inflated negative binomial model and geospatial integration.

The paper introduces a Bayesian model to detect spatially variable genes in imaging-based spatial transcriptomics data, showing high accuracy in identifying genes with novel spatial patterns.

This paper introduces BayesDeep, a Bayesian model that reconstructs spatial transcriptomics data at single-cell resolution using histology images and SRT datasets.

ScopeViewer provides a browser-based tool for visualizing spatial transcriptomics data with features for interactive exploration, dual viewing, optimized bandwidth, and data security.

The paper introduces iIMPACT, a method that integrates histology images and molecular data to improve clustering and domain detection in spatial transcriptomics.

STAr provides an integrated platform with curated datasets and methods to evaluate and develop spatial transcriptomics analysis tools.

MB-SupCon integrates microbiome and metabolomics data to improve prediction accuracy in microbiome-only datasets, achieving high accuracy in predicting insulin resistance, sex, and race.

MetaPrism enables joint analysis of taxa and gene abundances in metagenomic data to identify potential biomarkers for therapeutic response prediction.

MB-GAN generates synthetic microbiome data that mimic real datasets, preserving key statistical properties for method development.

HARMONIES infers sparse microbiome networks using a zero-inflated negative binomial model and regularization, outperforming existing methods and identifying disease-associated microbial communities.