Abstract
Over the years, medicine has continuously tried to provide the best treatment for patients. In cancer treatment, science has been at the service of medicine, to treat and discover new and better treatment approaches to “save lives” or increase longevity. Among the new disciplines that have been used to try to meet these challenges are Big Data, Bioinformatics, Machine Learning, Data Mining, Pharmacogenomics and Genomics. Whole genome sequencing has led medicine into several fields, such as broad knowledge of the human body, high accuracy in detecting a pathology or the most promising therapeutic line for a particular individual. The aim of the study is to understand and evaluate the main similarity metrics that could eventually be used in drug recommendation systems in cancer patients. Ten of the most popular similarity indices were used in the evaluation of gene expression of twenty samples from the Genomics of Drug Sensitivity in Cancer (GDSC) Project dataset, concerning breast and skin cancer pathology. The results obtained from the tested similarity indices are discussed, proposing some of them for the mentioned types of recommendation systems.
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