Maycon Marção

STAMP enables low-cost, high-throughput single-cell analysis by integrating transcriptomics and proteomics imaging, supporting both single and multimodal profiling of diverse cell types.

SAHA creates a multimodal, subcellular-resolution reference atlas of healthy human tissues, revealing spatial organization and disease-associated disruptions across multiple organ systems.

The paper introduces two machine-learning models to assess stemness in gliomas using bulk gene expression data, revealing associations with prognosis and therapeutic resistance.

The paper applies a one-class logistic regression model to predict stemness in single-cell and spatial omics datasets without requiring cell annotation.