Clustering algorithms have become a popular way to analyze biomedical data sets and in particular, gene expression data. Since these data sets are often large, it is difficult to gather useful information from them as a whole. Clustering is a proven method to extract knowledge about the data that can eventually lead to many discoveries in the biological world. Hierarchical clustering is used frequently to interpret gene expression data, but recently, graph-theoretic clustering algorithms have started to gain some attraction for analysis of this type of data. We consider five graph-theoretic clustering algorithms run over a post-mortem gene expression dataset, as well as a few different biomedical data sets, in which the ground truth, or class label, is known for each data point. We then externally evaluate the algorithms based on the accuracy of the resulting clusters against the ground truth clusters. Comparing the results of each of the algorithms run over all of the datasets, we found that our algorithms are efficient on the real biomedical datasets but find gene expression data especially difficult to handle.
|Commitee:||Crk, Igor, McKenney, Mark|
|School:||Southern Illinois University at Edwardsville|
|School Location:||United States -- Illinois|
|Source:||MAI 54/04M(E), Masters Abstracts International|
|Subjects:||Bioinformatics, Computer science|
|Keywords:||Ahlert, Darla, Bioinformatics, Clustering, Computer science, Gene expression data|
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