Hierarchical Clustering, Languages and Cancer

In this paper, we introduce a novel objective function for the hierarchical clustering of data from distance matrices, a very relevant task in Bioinformatics. To test the robustness of the method, we test it in two areas: (a) the problem of deriving a phylogeny of languages and (b) subtype cancer classification from microarray data. For comparison purposes, we also consider both the use of ultrametric trees (generated via a two-phase evolutionary approach that creates a large number of hypothesis trees, and then takes a consensus), and the best-known results from the literature. We used a dataset of measured 'separation time' among 84 Indo-European languages. The hierarchy we produce agrees very well with existing data about these languages across a wide range of levels, and it helps to clarify and raise new hypothesis about the evolution of these languages. Our method also generated a classification tree for the different cancers in the NCI60 microarray dataset (comprising g...

• Type: Conference
• Year: 2006
• Where: EVOW
• Authors: Pritha Mahata, Wagner Costa, Carlos Cotta, Pablo Moscato
• Keywords: Artificial Intelligence, Cancer Cell Lines, EVOW 2006, Subtype Cancer Classification, Two-phase Evolutionary Approach