ArticleFundamental research2026
Unsupervised and supervised machine learning to identify variability of tumor-educated platelets and association with pan-cancer: A cross-national study.
Article in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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15 authors.
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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 robustness and generality, reflected by comparable patterns of important gene expression, cancer type prevalence, and biological annotation across derivation, evaluation and validation cohorts. Cluster 1 (
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