Chao Li

This paper introduces HARMONIC, a framework that improves cell-cell communication inference in spatial transcriptomics by incorporating histology data, showing it better captures anatomical context.

FOCUS improves spatial transcriptomics by coordinating molecular and spatial fidelity across multiple tasks and datasets, revealing clearer biological patterns in immunology, development, and oncology.

This study uses whole-genome sequencing to identify genetic variants associated with brain imaging phenotypes, finding significant associations at both variant and gene levels.

This study identifies bidirectional genetic links between glycemic traits and brain phenotypes using Mendelian randomization and validates findings in UK Biobank data.

The paper uses graph neural networks to predict functional connectivity from structural connectivity in human brain networks, finding strong group-common structure-function coupling with some individual-specific effects.

This paper quantifies structural connectome disruptions in glioblastoma patients using diffusion MRI and shows their association with worse prognosis and survival.

The paper quantifies low perfusion compartments in glioblastoma using MRI and correlates them with patient survival outcomes.

This study uses joint histogram analysis of diffusion tensor imaging to identify tumor infiltration patterns in glioblastoma and their prognostic significance.

This paper integrates multi-parametric and multi-regional MRI histogram features to identify patient subgroups with distinct survival outcomes in glioblastoma.