Application of Fast Integration Strategy for Multi-Omics Data and Limited Random Forest Model in Survival Prediction of Glioblastoma.

Ze Liu¹, Yanhui Wu², Shanshan Wang³, Fei Lin⁴, Lin Wan⁵, Peng Song⁶

¹College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.

²International Department, Jinan Foreign Language School, Jinan, Shandong, China.

³Department of Public Health, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.

⁴School of Continuing Education, Shandong University, Jinan, Shandong, China.

⁵School of Software, Shandong University, Jinan, China.

⁶Department of MedicaI Oncology, The Second Medical Center and NationaI CIinicaI Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.

PubMed PMC DOI PDF

Abstract

Gliomas exhibit significant prognostic heterogeneity, and single-omics data/existing technologies struggle to balance multi-omics integration efficiency, prediction accuracy, and clinical adaptability-hindering the clinical translation of precise prognostic assessment. Focussing on glioblastoma (GBM) and lower-grade glioma (LGG), this study proposes an integrated solution: three-step multi-omics f…

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