GE-Miner: integration of cluster ensemble and text mining for comprehensive gene expression analysis.
Generating high quality gene clusters and identifying the underlying biological mechanism of the gene clusters are the important goals of clustering gene expression analysis. Based on this consideration, we design and develop a unified system Gene Expression Miner (GE-Miner) by integrating cluster ensemble, text clustering and multidocument summarisation and provide an environment for comprehensive gene expression data analysis. Experimental results demonstrate that our systems can obtain high quality clusters and provide concise and informative textual summary for the gene clusters.
Hu
Xiaohua
X
College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA. thu@ischool.drexel.edu
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003201:Computers; D003628:Database Management Systems; D030562:Databases, Protein; D015870:Gene Expression; D020869:Gene Expression Profiling; D016247:Information Storage and Retrieval; D005810:Multigene Family; D009323:Natural Language Processing; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D012984:Software