Unsupervised and supervised machine learning to identify variability of tumor-educated platelets and association with pan-cancer: A cross-national study.

ABSTRACT: Cancer can educate platelets by altering transcriptome profiles. However, the exact education mechanism remains unclear, and the variability of tumor-educated platelet (TEP) transcriptome has not been investigated. In this study, we aimed to build a stratification system for TEP based on machine learning (ML) data-driven patterns and platelet transcriptome profiles. This study included platelet samples from 1,628 cancer participants from European and United States populations, including 18 different and most prevalent types of cancer. Gaussian mixture model (GMM) was used to identify robust clusters and similar education pattern. While extreme gradient boosting (XGBoost) was used to precisely predict the clusters. Three clusters were eventually identified. The cluster results showed robust

SUBMITTER: Chen X 

PROVIDER: S-EPMC12869750 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature