Alia Shamikh

This study uses deep learning to classify pediatric brain tumors using H&E and Ki-67 images, finding that multi-stain fusion improves classification accuracy compared to single-stain models.

The paper creates a spatial transcriptomic atlas of pediatric brain tumors, identifying spatial archetypes and relapse-associated clones across multiple diagnoses.

This study tracks microglial state changes and neuronal asynchrony after irradiation in mice, showing delayed microglial responses and two phases of neuronal dysfunction.

The paper introduces RRST, a spatial transcriptomics workflow that improves mRNA recovery from low-quality fresh frozen samples, demonstrating its effectiveness across multiple tissue types.

The paper uses spatial transcriptomics to analyze copy number variations in benign and malignant tissues, revealing clonal patterns and suggesting early cancer evolution mechanisms.