Min Tang, Shan Jiang, Xiaoming Huang, Chunxia Ji, Yexin Gu, Ying Qi, Yi Xiang, Emmie Yao, Nancy Zhang, Emma Berman, Di Yu, Yunjia Qu, Longwei Liu, David Berry, Yu Yao
Glioma, with its heterogeneous microenvironments and genetic subtypes, presents substantial challenges for treatment prediction and development. We integrated 3D bioprinting and multi-algorithm machine learning as a novel approach to enhance the assessment and understanding of glioma treatment responses and microenvironment characteristics. The bioprinted patient-derived glioma tissues successfully recapitulated molecular properties and drug responses of native tumors. We then developed GlioML, a machine learning workflow incorporating nine distinct algorithms and a weighted ensemble model that generated robust gene expression-based predictors, each reflecting the diverse action mechanisms of various compounds and drugs...
April 9, 2024: Cell Discovery