Fuzzy c-means clustering with prior biological knowledge
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- 作者：Luis Tari ; Chitta Baral ; Seungchan Kim
- 关键词：Semi-supervised clustering ; Gene function prediction ; Fuzzy c-means clustering ; Gene Ontology ; Gene expression data ; Saccharomyces cerevisiae yeast
- 刊名：Journal of Biomedical Informatics
- 出版年：2009
- 出版时间：February 2009
- 年：2009
- 卷：42
- 期：1
- 页码：74-81
- 全文大小：302 K
文摘
We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike traditional clustering methods, our method is capable of assigning genes to multiple clusters, which is a more appropriate representation of the behavior of genes. Two datasets of yeast (Saccharomyces cerevisiae) expression profiles were applied to compare our method with other state-of-the-art clustering methods. Our experiments show that our method can produce far better biologically meaningful clusters even with the use of a small percentage of Gene Ontology annotations. In addition, our experiments further indicate that the utilization of prior knowledge in our method can predict gene functions effectively. The source code is freely available at http://sysbio.fulton.asu.edu/gofuzzy/.