Tari Luis, Baral Chitta, Kim Seungchan
School of Computing and Informatics, Department of Computer Science and Engineering, Ira A. Fulton School of Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ 85287-8809, USA.
J Biomed Inform. 2009 Feb;42(1):74-81. doi: 10.1016/j.jbi.2008.05.009. Epub 2008 May 24.
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/.
我们提出了一种名为GO模糊c均值的新型半监督聚类方法，该方法能够在概率聚类算法中同时利用生物知识和基因表达数据。我们的方法基于模糊c均值聚类算法，并利用基因本体注释作为先验知识来指导功能相关基因的分组过程。与传统聚类方法不同，我们的方法能够将基因分配到多个簇中，这更恰当地表示了基因的行为。应用两个酵母（酿酒酵母）表达谱数据集将我们的方法与其他最先进的聚类方法进行比较。我们的实验表明，即使仅使用一小部分基因本体注释，我们的方法也能产生更具生物学意义的簇。此外，我们的实验进一步表明，我们方法中先验知识的利用能够有效地预测基因功能。源代码可在http://sysbio.fulton.asu.edu/gofuzzy/免费获取。
