Saugato Rahman

Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. Whereas molecular data provide comprehensive insights, obtaining such data remains costly and time-consuming. In this study, we developed Path2Omics, a deep learning framework that independently predicts gene expression and methylation from histopathology across 30 The Cancer Genome Atlas cancer types.

Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. While molecular data provide comprehensive insights, obtaining such data remains costly and time-consuming. Here, we developed Path2Omics, a deep learning framework that independently predicts gene expression and methylation from histopathology across 30 TCGA cancer types.