Junpeng Zhang

This study develops a hidden causality method to infer dynamic lncRNA regulation in ASD, revealing key lncRNAs and their roles in disease pathogenesis and neuroimmune interactions.

TACO detects presymptomatic Long COVID by combining causal inference with a foundation model, achieving higher precision with fewer features and no hyperparameter tuning.

This paper reviews computational methods and datasets for understanding Long COVID's mechanisms and developing diagnostic and therapeutic strategies using multi-omics data.

This paper identifies 32 causal genes for Long COVID using a multi-omics framework combining TWMR and CT, revealing novel genetic factors and symptom subtypes.

The paper introduces ctsGRN, a framework for inferring cell type-specific gene regulatory networks in Arabidopsis roots using multiple computational methods, and identifies key transcription factors and regulatory modules.

Cycle identifies cell type-specific lncRNA regulatory networks in ASD, revealing rewired and conserved modules and hubs associated with ASD-related functions.

The paper introduces Scan, a framework that infers sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data using multiple network inference methods.

This paper introduces SCOM, a framework to identify ncRNA synergistic competition across cancers, revealing hub ncRNAs and potential biomarkers for diagnosis and prognosis.

The paper introduces CSmiR, a method to identify cell-specific miRNA-mRNA regulatory networks using single-cell co-sequencing data, revealing unique miRNA regulation in individual cells.

LMSM identifies lncRNA-related miRNA sponge modules in breast cancer using heterogeneous data, finding subtype-specific modules and improved classification performance over baselines.

The paper introduces miRsyn, a causal inference framework to identify miRNA synergism using observational data, finding that many miRNA pairs with shared targets do not synergize at the expression level.

miRspongeR is an R/Bioconductor package that identifies and analyzes miRNA sponge interaction networks and modules using seven methods and an integrative approach.

This paper identifies miRNA-mRNA regulatory relationships in breast cancer using invariant causal prediction and shows improved performance over existing methods.

miRBaseConverter provides tools to convert and retrieve miRNA information across miRBase versions, addressing inconsistencies in miRNA data.