It has been observed that genes with the same function or involved in the same biological process are likely to co-express, hence clustering gene expression profiles provide a means for gene function prediction. Most existing clustering methods ignore known gene functions in the process of clustering, and also get the analysis results lacking of stability and biological interpretability. To make full use of the accumulating gene function annotations, we propose using the density information of genes and known biological knowledge through the density based algorithms, which can get a better clustering result than the traditional clustering algorithms. An application to two real datasets demonstrates the advantage of our proposal over the standard method.
['Sun Hang', 'Zhou You', 'Liang Yan Chun']
Incorporating Biological Knowledge into Density-Based Clustering Analysis of Gene Expression Data