This talk will familiarize attendees with several clustering methods in the context of medicine. This kind of unsupervised algorithms are used for finding homogeneous groups (clusters) of objects. Samples forming one group should be similar to each other but different between other clusters.
Such methods are successfully used in the field of medicine and biology. One of successful applications is grouping genes having similar expression patterns. For example, the agglomerative clustering (with average linkage and correlation as a similarity metric) is applied to NCI60 data (containing gene expressions for 60 tumor cell lines), and, as a result, a dendrogram is obtained which allows to see groups of similar cell lines.
There are more applications of clustering, like better missing values imputation, outliers detection or finding subpopulations in patients’ data, to name a few, and will be presented with an explanation of clustering algorithms applied.
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