Relationship preserving feature selection for unlabelled clinical trials time-series
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
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Clustering has been shown to be effective in analyzing functional relationships of genes. However, no single clus- tering method with single distance metric is capable of cap- turing all types of relationships that a gene may have with other genes. In this paper we introduce a framework which groups genes around a query gene, and ranks them in or- der corresponding to different levels of similarity utilizing multiple metrics. The focus of our efforts is to create gene centric clusters. The notion of Strong Group (SG) is pre- sented as a cluster definition where no two genes are dis- tant from each other, greater than a threshold value. The genes are then ranked on their frequency of co-occurrence. The grouping and rankings are drawn by applying set op- erations over results of multiple distance metrics, each cap- turing particular similarities such as shifted relationships, negative correlations and strong positive relationships. The effectiveness of the algorithm is demonstrated on two case studies. In the first one, a single yeast cell cycle dataset is used. It is shown that different combination of set opera- tions reveals different kinds of interactions between genes. Finally, to provide further analysis on our techniques, we have tested them on multiple microarray datasets obtained from Stanford Microarray Database.