vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff9fa925000000fd36030001000200
A major protest in cancer treatment is predicting the clinical response to anti-cancer drugs for each individual patient. For complex diseases such as Myeloma, characterized by high inter-patient variance, the implementation of precision medicine approaches is dependent upon understanding the pathological processes at the molecular level. Myeloma is one of the horrible diseases in the world claiming plurality of lives. Accurately predicting drug responses to Myeloma is a most important problem preventing oncologists’ efforts to ensemble the most powerful drugs to treat Myeloma, which is a root goal in precision medicine. It entails the design of therapies that are matched for each individual patient. In this article, it considers a review of approaches that have been proposed to tackle the drug sensitivity prediction problem especially with respect to the personalized Cancer therapy. There are a total of 44 drug sensitivity prediction algorithms. In that the gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. The proposed algorithm surpassed Bayesian Multitask Multiple Kernel Learning (BMMKL) classification which currently represent the state-of-the-art in drug-response prediction and finally passed the gene expression data to Cytoscape for visualization.