Mingyang Lu

The paper introduces NetDes-Duo, a method to jointly infer gene regulatory networks from scRNA-seq data, revealing distinct cell-state landscapes in neutrophil reprogramming under different conditions.

This paper introduces NetDes, a method to infer gene regulatory networks and build dynamical models from single-cell gene expression data, showing its effectiveness in reconstructing cell state transitions.

This paper introduces a method to predict reversible and irreversible cell state transitions using gene regulatory networks and gene expression data.

The paper develops a computational approach to model core gene regulatory networks in IDH mutant AML, identifying key regulators like DNMT1 and E2F1 supported by patient survival data.

This study evaluates topological motifs and their coupling in gene circuits using state distributions to identify regulatory interactions involved in human neuron differentiation.

This paper introduces SacoGraci, a data-driven method for coarse-graining large gene regulatory networks using ensemble-based modeling and MCMC optimization, demonstrating its effectiveness on synthetic and literature-based networks.

NetAct constructs core transcription factor regulatory networks using transcriptomics data and literature databases, outperforming existing methods in network inference.

The paper uses random parametric perturbations of a gene regulatory circuit in budding yeast to uncover state transitions during the cell cycle, showing consistency with single-cell RNA-seq data.

The paper constructs a gene regulatory network model for EMT hybrid states in mouse embryonic skin cells using scRNA-seq data and literature-based interactions, identifying Wnt signaling as a key pathway.

The paper uses computational methods to classify gene expression profiles into epithelial, hybrid E/M, and mesenchymal categories based on EMT dynamics and scoring metrics.

The paper develops sRACIPE, a method combining stochastic analysis with random circuit perturbation to study noise effects in gene regulatory circuits, finding that high noise merges clusters rather than large parameter variation.

The paper models the coupling of gene regulation and metabolic pathways in cancer, showing AMPK and HIF-1 activity correlates with metabolic pathway activities in tumor samples.

RACIPE is a computational tool that models gene regulatory circuits by randomizing parameters to identify robust dynamical features without requiring detailed kinetic data.

The paper introduces RACIPE, a computational method to assess the robustness of gene regulatory circuits by analyzing their dynamical behavior under parameter perturbations, showing topology dominates over kinetic parameters.