The article reviews the application of Support Vector Machine (SVM) learning in cancer genomics, highlighting its role in classifying and subtyping cancers, biomarker discovery, drug discovery, and cancer driver gene identification. SVMs are powerful tools for recognizing subtle patterns in complex datasets, making them suitable for high-dimensional and noisy genomic data. The authors discuss the SVM model, its kernel methods, and various applications in cancer genomics. They also explore the challenges and limitations of SVMs, such as the computational cost and interpretability of the underlying models. The article emphasizes the importance of SVMs in advancing cancer research and developing personalized treatments.The article reviews the application of Support Vector Machine (SVM) learning in cancer genomics, highlighting its role in classifying and subtyping cancers, biomarker discovery, drug discovery, and cancer driver gene identification. SVMs are powerful tools for recognizing subtle patterns in complex datasets, making them suitable for high-dimensional and noisy genomic data. The authors discuss the SVM model, its kernel methods, and various applications in cancer genomics. They also explore the challenges and limitations of SVMs, such as the computational cost and interpretability of the underlying models. The article emphasizes the importance of SVMs in advancing cancer research and developing personalized treatments.