Ziqi Zhang

CeSpGRN infers cell-specific gene regulatory networks from single-cell multi-omics and spatial data, showing improved accuracy over existing methods.

SpaDecoder improves cell type deconvolution in spatial transcriptomics by leveraging 3D tissue structure and single-cell reference profiles.

scDisInFact disentangles batch and condition effects in scRNA-seq data, improving batch correction, gene detection, and perturbation prediction across multiple batches and conditions.

scMultiSim simulates multi-modal single-cell data by integrating cell-cell interactions and gene regulatory networks, enabling broader benchmarking of computational methods.

The paper introduces scDART, a deep learning framework that integrates scRNA-seq and scATAC-seq data and learns cross-modal relationships for trajectory inference.

scMoMaT integrates single-cell multi-omics data under mosaic integration using matrix tri-factorization, uncovering modality-specific biomarkers for improved cell type annotation.

CellPath infers high-resolution cell developmental trajectories using RNA velocity, allowing multiple paths and automatic root detection without topology constraints.

VeloSim simulates gene-expression and RNA velocity along trajectories to benchmark computational methods with controlled biological and technical variation.

The paper presents an Integer Linear Programming approach for optimal ancestral PPI network reconstruction under the DMC model, showing improved likelihood and agreement with biological evidence.