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Prostate cancer is widely known to be one of the most common cancers among men around the world. Due to its high heterogeneity, many of the studies carried out to identify the molecular level causes for cancer have only been partially successful. Among the techniques used in cancer studies, gene expression profiling is seen to be one of the most popular techniques due to its high usage. Gene expression profiles reveal information about the functionality of genes in different body tissues at different conditions. In order to identify cancer-decisive genes, differential gene expression analysis is carried out using statistical and machine learning methodologies. It helps to extract information about genes that have significant expression differences between healthy tissues and cancerous tissues.
The research addresses a comprehensive supervised classification approach using Support Vector Machine (SVM) models to investigate differentially expressed Y-chromosome genes in prostate cancer. It is proposed to design and develop a generic cancer metastasis detection tool with high accuracy and applicability to a wide range of cancer types.
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