Systematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method

Background: Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. However, there are still gaps toward whole-genome functional annotation of genes using the gene expression data. Results: In this paper, we propose a novel technique called Fuzzy Nearest Clusters for genomewide functional annotation of unclassified genes. The technique consists of two steps: an initial hierarchical clustering step to detect homogeneous co-expressed gene subgroups or clusters in each possibly heterogeneous functional class; followed by a classification step to predict the functional roles of the unclassified genes based on their corresponding similarities to the detected functional clusters. Conclusion: Our experimental results with yeast gene expression data showed that the proposed method can accurately predict the genes' functions, even those with multiple functional roles, and the pr...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Xiaoli Li, Yin-Chet Tan, See-Kiong Ng
• Keywords: BMCBI 2006, Functional Roles, Gene Expression Data, Unclassified Genes