Bingqiang Liu

DiffGSP reverses mRNA diffusion to improve spatial transcriptomics fidelity, revealing fine anatomical structures and spatial gene modules in various tissues.

SpaGRD models cell-cell communication using graph reaction-diffusion systems, revealing spatially resolved signaling patterns in spatial transcriptomics data.

SLECA creates a single-cell atlas of SLE to identify rare cell types, finding expanded double-negative T cells linked to disease severity and showing potential for therapeutic reprogramming.

virCHap reconstructs viral haplotypes from long-read sequencing data, outperforming existing methods in recall, abundance estimation, and read clustering.

MNetClass identifies central microbial subcommunities across ecological niches without control samples, outperforming existing methods in accuracy and predictive power.

The paper integrates 16S rRNA sequencing data across cohorts to identify consistent microbial signatures in AD and develops predictive models with high diagnostic accuracy.

This paper introduces MICAH, a graph neural network framework that identifies cancer-associated microbial communities in intratumoral microbiome data across five cancer types.

STREAM infers enhancer-driven gene regulatory networks from single-cell RNA-seq and ATAC-seq data, showing improved performance in TF recovery and enhancer-gene discovery.

TESA improves TFBS identification in ChIP-exo data by integrating nucleotide-resolution signal with a two-stage alignment framework, showing higher accuracy in E. coli datasets.

MarsGT identifies rare cell populations in multi-omics single-cell data, outperforming existing tools and revealing novel biological insights in human and mouse datasets.

This paper introduces IDAM, a method for inferring disease-associated microbial gene modules from metagenomic and metatranscriptomic data, showing superior performance and reproducibility in inflammatory bowel disease datasets.

CEMIG predicts cis-regulatory motifs from ATAC-seq data, outperforming existing methods and identifying cell-type-specific and shared TF motifs in GM12878 and K562 cells.

This paper introduces SpaGFT, a graph Fourier transform model for spatial omics data analysis, enabling accurate identification of tissue modules and their functional relationships in spatial transcriptomics and proteomics data.

RESEPT uses deep learning to visualize and characterize tissue architecture from spatial transcriptomics data, revealing pathological structures in Alzheimer's and glioblastoma samples.

DeepMAPS infers biological networks from single-cell multi-omics data using a heterogeneous graph transformer, outperforming existing tools in cell clustering and pathway prediction.

This paper introduces a deep forest-based method for predicting protein-protein interactions, achieving high accuracy in yeast and Helicobacter pylori and improving drug discovery.

The paper introduces a computational framework, SeqATU, to predict alternative transcription units in bacterial genomes using RNA-Seq data, achieving high precision and recall in E. coli.

This paper introduces a PPI prediction method using gradient tree boosting and L1-regularized logistic regression, achieving high accuracy on multiple organisms.

The paper introduces DESSO, a deep learning framework for predicting regulatory motifs from ChIP-Seq data, showing improved accuracy over existing tools.

QUBIC2 improves biclustering for bulk and single-cell RNA-Seq by addressing sparsity and transcriptional regulation, outperforming existing tools in functional module detection and cell type classification.

ViDGER provides visualization tools to interpret DGE results from RNA-seq data using Cuffdiff, DESeq2, and edgeR.