Using Support Vector Machines (Svm) to Facilitate Classification of Cancer
Journal Title: Enliven: Journal of Anesthesiology and Critical Care Medicine - Year 2018, Vol 5, Issue 3
Abstract
Scientists are now able to concurrently perform screening or expression of many genes using DNA micro-arrays to establish their status that is whether they are silent, hyperactive, or active in cancerous or normal tissue [1]. Given the fact that such new micro-array devices produce massive volumes of critical data, there is a need to develop new analytical methods that are capable of sorting out whether the cancer cell shave unique signatures of gene tissue over the several types of cancer tissue or the normal cells. The current multiple data sets which are publicly available on open source platforms such as Internet are characterized by many problems, for instance,a rather smaller number of experiments and a huge amount of gene expression values per experiment. Additionally, it is evident that data analysis can be conducted from diverse perspectives. The majority of the empirical studies in the available relate to gene clusters which have been revealed through unsupervised learning methodologies. Clustering is usually conducted together with other data dimensions; for instance, each experiment might link to one patient who might carry or not have a certain disease. Therefore, clustering is mostly done with the intention of grouping the patients with comparable clinical history or record. Presently, it is evident that the application of supervised learning to classify cancer and proteins has increasingly gained prominence.
Authors and Affiliations
Ray Nick
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