An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes

20 Nov 2018Nora K. SpeicherNico Pfeifer

Due to the complexity of cancer, clustering algorithms have been used to disentangle the observed heterogeneity and identify cancer subtypes that can be treated specifically. While kernel based clustering approaches allow the use of more than one input matrix, which is an important factor when considering a multidimensional disease like cancer, the clustering results remain hard to evaluate and, in many cases, it is unclear which piece of information had which impact on the final result... (read more)

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