Support Vector Machine (SVM) learning is a powerful classification tool used in cancer genomics for classifying or subtyping cancers. This review summarizes recent progress in applying SVMs to cancer genomic studies. SVMs are effective in identifying patterns in complex genomic data, such as gene expression, DNA methylation, and protein interactions, which are crucial for discovering biomarkers, drug targets, and understanding cancer driver genes. SVMs can handle high-dimensional data and are particularly useful in cancer studies due to their ability to detect subtle patterns in noisy data. SVMs are used for feature selection, classification, and prediction in cancer genomics. They have been applied to various cancer types, including leukemia, colon cancer, breast cancer, and soft tissue sarcomas. SVMs can classify cancer subtypes and predict outcomes based on gene expression and methylation data. They are also used in drug discovery for cancer therapy, identifying potential drug targets and predicting drug sensitivity. SVMs are effective in identifying cancer driver genes, which are mutations that contribute to cancer progression. They can distinguish between driver and passenger mutations, which are mutations that do not contribute to cancer. SVMs are also used in gene-gene interaction analysis and in predicting protein-protein interactions, which are important for understanding cancer biology. The review highlights the versatility of SVMs in cancer genomics, including their use in biomarker discovery, drug target identification, and cancer classification. SVMs are also used in multi-omics data integration, combining data from different omics layers to improve the accuracy of cancer diagnosis and treatment. Despite their effectiveness, SVMs have some limitations, such as computational complexity and the need for careful parameter tuning. However, they remain a valuable tool in cancer genomics for their ability to handle complex data and provide insights into cancer biology.Support Vector Machine (SVM) learning is a powerful classification tool used in cancer genomics for classifying or subtyping cancers. This review summarizes recent progress in applying SVMs to cancer genomic studies. SVMs are effective in identifying patterns in complex genomic data, such as gene expression, DNA methylation, and protein interactions, which are crucial for discovering biomarkers, drug targets, and understanding cancer driver genes. SVMs can handle high-dimensional data and are particularly useful in cancer studies due to their ability to detect subtle patterns in noisy data. SVMs are used for feature selection, classification, and prediction in cancer genomics. They have been applied to various cancer types, including leukemia, colon cancer, breast cancer, and soft tissue sarcomas. SVMs can classify cancer subtypes and predict outcomes based on gene expression and methylation data. They are also used in drug discovery for cancer therapy, identifying potential drug targets and predicting drug sensitivity. SVMs are effective in identifying cancer driver genes, which are mutations that contribute to cancer progression. They can distinguish between driver and passenger mutations, which are mutations that do not contribute to cancer. SVMs are also used in gene-gene interaction analysis and in predicting protein-protein interactions, which are important for understanding cancer biology. The review highlights the versatility of SVMs in cancer genomics, including their use in biomarker discovery, drug target identification, and cancer classification. SVMs are also used in multi-omics data integration, combining data from different omics layers to improve the accuracy of cancer diagnosis and treatment. Despite their effectiveness, SVMs have some limitations, such as computational complexity and the need for careful parameter tuning. However, they remain a valuable tool in cancer genomics for their ability to handle complex data and provide insights into cancer biology.