Ziyi Li

This study identifies stemness features in luminal hormone-responsive cells within DCIS that correlate with invasive progression and shows that targeting FOXA1 reduces tumor growth.

SuperSurv provides a unified R package for building, evaluating, and interpreting ensemble survival models with right-censored data, using SHAP values and IPCW Brier risk for weighting.

STpath translates histopathology image embeddings into cell-type composition and gene expression estimates for clinical applications.

TUSCAN improves tumor region identification in spatial transcriptomics by integrating copy number variation profiles with histopathological images.

UniST reconstructs dense 3D spatial transcriptomics data from sparse serial sections, restoring structural continuity and biologically meaningful expression patterns in multiple tissue contexts.

This paper compares statistical methods for differential gene expression in spatial transcriptomics, finding that the GEE framework better controls false positives than the Wilcoxon test.

The paper compares two zebrafish strains using multi-omic data to identify genetic and regulatory differences affecting circadian rhythms and immune response.

PoweREST estimates statistical power for detecting differentially expressed genes in spatial transcriptomics experiments using 10X Genomics Visium data.

The paper profiles proteome and phosphoproteome dynamics during neuronal differentiation of the KOLF2.1J iPSC line, revealing distinct molecular signatures and kinase activity changes.

The paper identifies leopard cats as the earliest felines in Chinese human settlements, predating domestic cats by over 2,500 years, and traces the arrival of domestic cats via the Silk Road.

This study shows riluzole restores blood-testis barrier function and rescues chemotherapy-induced male infertility in mice by targeting TRPC5.

This study compares acute gastrointestinal toxicity from synchrotron-based proton and linac-based electron ultra-high dose rate irradiation in mice, finding higher toxicity with proton FLASH compared to conventional and electron treatments.

The paper introduces ICeITH, a Bayesian model to assess intratumor heterogeneity of tumor-infiltrating leukocytes using multi-region gene expression data, showing improved accuracy over existing methods.

This study evaluates how varying radiation beam parameters affect gastrointestinal toxicity and tumor response in mice, finding that high dose per pulse and ultra-high dose rate independently reduce toxicity while maintaining tumor control.

MASH identifies high-dimensional mediators in survival analysis, showing cigarette smoking mediates 51.1% of coronary heart disease risk via five metabolites.

CASi is a framework for analyzing multi-timepoint scRNA-seq data, enabling cell annotation, novel cell type detection, population evolution visualization, and temporal differential gene identification.

CeDAR improves cell-type-specific differential analysis in bulk omics data by incorporating cell type hierarchy, enhancing accuracy and power, especially for low abundance cell types.

The paper models scRNA-seq data from developmental processes using SDEs to identify transition cells that explain heterogeneity and predict cell fate in early tumorigenesis.

This paper introduces a method for imputing censored survival data to improve machine learning analysis in biomedical research.

NeuCA improves scRNA-seq cell annotation by using a neural network to leverage hierarchical cell type structure, achieving better accuracy than existing methods.

This paper applies machine learning to response-adaptive randomization in clinical trials, showing improved treatment assignment and response rates.

The paper introduces a single-cell proteomic workflow that achieves high sensitivity and throughput for protein quantification in human cell lines.

The paper develops a method to detect differential signals in high-throughput data from mixed cell-type samples and shows its superior performance through simulations and real datasets.