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arXiv AI By Ahmed Ammar Kubba, Manar Abu Talib, Jibran Sualeh Muhammad, Ali Bou Nassif, Abdalla Sayed Mohamed, Darko Castven, Jens U. Marquardt

Semi-Supervised Learning-Based Genetic Biomarkers Dataset for Multiple-Stage Hepatocellular Carcinoma Prediction

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The article introduces a new dataset for hepatocellular carcinoma (HCC) prediction, comprising 770 patient samples with 11,150 gene expression levels each, categorized into five classes from normal tissue to various HCC stages. The dataset was constructed using XGBoost and semi‑supervised learning on three existing genomic biomarker datasets, leveraging their labels to generate new annotations. The resulting XGBoost model achieved a 96.5% classification accuracy during the semi‑supervised training process.

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