Article

Multiple instance learning with spatial transcriptomics for interpretable patient-level predictions: application in glioblastoma

2025-10-15

Abstract excerpt

Accurate prediction of patient outcomes remains a major challenge in oncology. While recent machine learning (ML) approaches often rely on bulk omics lacking spatial resolution or histology-based multiple instance learning (MIL), spatial transcriptomics (SpT) provides a unique opportunity to capture both molecular content and tissue architecture. However, no generalizable ML framework has yet been established to e...

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Literature Corpus work: ae53240f-78ab-5c1a-bc10-53147aff3e18
DOI: 10.1101/2025.10.13.682206

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