Cancer is a complex genetic disease as a result of accumulation of mutations. The study of these mutations not only reveals the molecular mechanism of tumoriogenesis but also provides insight for the development of novel therapeutics and diagnostic approaches. Missense mutations are among the most common types of mutations and can cause a various types of cancer. Deleterious effects of missense mutations are usually attributed to changes in the primary amino acid sequence, protein structure and function. Identifying the detrimental missense mutations in cancer genes and their drug targets are the challenging task in cancer biology. As missense variants are commonly identified in genomic sequence, only a small fraction directly contributed to oncogenesis. The ability to distinguish those missense changes that contribute to cancer progression and drug resistance from those that do not is a difficult problem usually accomplished through functional in vivo analysis. Hence, this book provides a computational protocol for identifying the detrimental missense mutation of various cancer genes in a lucid manner.