Robust Cluster Prediction Across Data Types Validates Association of Sex and Therapy Response in GBM.

• Tác giả: Boris Aguilar, Jill Barnholtz-Sloan, Gino Cioffi, David L Gibbs, Jingqin Luo, Jacob Mandel, Edward Pan, David Pot, Joshua B Rubin, Yoshie Umemura, Kristin A Waite
• Ngôn ngữ: eng
• Ký hiệu phân loại: 519.287 Expectation and prediction
• Thông tin xuất bản: Switzerland : Cancers , 2025
• Bộ sưu tập: NCBI
• ID: 75894

Tóm tắt

BACKGROUND: Previous studies have described sex-specific patient subtyping in glioblastoma. The cluster labels associated with these "legacy data" were used to train a predictive model capable of recapitulating this clustering in contemporary contexts. METHODS: We used robust ensemble machine learning to train a model using gene microarray data to perform multi-platform predictions including RNA-seq and potentially scRNA-seq. RESULTS: The engineered feature set was composed of many previously reported genes that are associated with patient prognosis. Interestingly, these well-known genes formed a predictive signature only for female patients, and the application of the predictive signature to male patients produced unexpected results. CONCLUSIONS: This work demonstrates how annotated "legacy data" can be used to build robust predictive models capable of multi-target predictions across multiple platforms.