Zhixiang Lin

RoBep predicts B-cell epitopes by modeling spatial continuity, improving performance on benchmark datasets and providing antibody-antigen binding regions.

The paper introduces SpaMV, a method for integrating spatial multi-omics data that preserves modality-specific information and improves clustering and dimensionality reduction.

INSIDER is a statistical framework for analyzing bulk RNA expression data that models interactions between biological variables and decomposes variation into a shared latent space to improve interpretation and downstream analysis.

SR2 learns sparse representations for scRNA-seq data integration, capturing gene expression patterns under heterogeneous conditions and identifying cell identities effectively.

SpatialScope integrates spatial and single-cell transcriptomics data using deep generative models to achieve single-cell resolution and infer transcriptome-wide expression levels.

This paper introduces MR-APSS, a Mendelian Randomization method that addresses pleiotropy and sample structure to improve causal inference from GWAS summary statistics.

Portal integrates large-scale single-cell datasets efficiently and accurately by preserving biological variation across samples, platforms, and species.

FIRM integrates heterogeneous scRNA-seq datasets across tissues and platforms, accurately preserving cell type identities and structure.

scAMACE integrates chromatin accessibility, gene expression, and methylation data to improve cell type clustering through a joint analysis approach.

coupleCoC+ improves clustering performance by integrating multimodal single-cell genomic data through transfer learning.

This paper introduces RA3, a reference-guided method for analyzing single-cell chromatin accessibility data, showing improved performance across multiple datasets.

This paper introduces a coupled co-clustering-based transfer learning method for integrating multimodal single-cell genomic data, improving clustering performance and matching cell subpopulations across data types.

This paper introduces scABC, an R package for unsupervised clustering of scATAC-seq data to classify cell types based on open chromatin regions.

AC-PCA reduces dimensionality and adjusts for confounding variation in biological datasets, recovering anatomical structure and shared variation across species.