Fang Wang

UniPert-G2CP transfers genetic perturbation insights to chemical perturbation modeling, enabling efficient in silico drug screening with reduced data costs.

This paper develops a photo-thermal coupling model to improve optical dose prediction in transcranial photobiomodulation by accounting for temperature-dependent changes in light propagation.

This study identifies GLUT1 as a driver of immunotherapy resistance in ESCC by epigenetically suppressing senescence and reveals dietary glucose restriction as a potential strategy to enhance immunotherapy efficacy.

The paper constructs a single-cell and spatial atlas of glioblastoma resistance, identifying a 'Spatial Resistance Triad' and demonstrating that targeting stromal components can overcome therapeutic resistance.

The paper shows archaea, specifically Bathyarchaeia, dominate deep sediments by prioritizing maintenance over growth, leading to long-term organic carbon preservation.

This paper introduces robust lesion network mapping (rLNM) to address statistical validity concerns in lesion network mapping, showing it recovers distinct symptom-specific networks and predicts postoperative language decline.

This study identifies Sphk1-S1P signaling as a driver of BBB breakdown after ICH through HIF-1α-dependent upregulation of Bsg-MMP-9, offering a therapeutic target.

This paper develops an EV-based platform for efficient circRNA delivery, enabling sustained protein expression and therapeutic applications in dendritic cell targeting and myocardial fibrosis.

This paper introduces a two-stage digital twin brain model constrained by EEG and fMRI data, showing it captures multi-scale brain dynamics in healthy and Alzheimer's disease cohorts.

The paper develops a neurocomputational framework to predict recurrence risk in first-episode depression using resting-state fMRI and identifies circuit-level neural correlates of relapse.

scGRIP improves gene regulatory network inference in single-cell multi-omics data by integrating prior knowledge and explainable AI techniques.

This study analyzes whole-genome sequencing data from 332 Chinese COVID-19 patients to identify genetic variants associated with disease severity and susceptibility.