Christian Esposito

This paper introduces SpaMIL, a spatial transcriptomics-based multiple instance learning framework for patient-level outcome prediction in glioblastoma, achieving high prognostic accuracy.

The paper identifies a prognostic infiltration zone in glioblastoma using histology and spatial transcriptomic integration, revealing specific cell composition associated with long-term survival.

This study evaluates deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data, finding that non-DL methods perform comparably on survival tasks while DL methods excel in gene essentiality prediction.