Xiaohua Hu
ABSTRACT
In this paper, we design and develop a unified system GE-Miner (Gene Expression Miner) to integrate cluster ensemble, text clustering and multi document summarization and provide an environment for comprehensive gene expression data analysis. We present a novel cluster ensemble approach to generate high quality gene cluster. In our text summarization module, given a gene cluster, our Expectation Maximization (EM) based algorithm can automatically identify subtopics and extract most probable terms for each topic. Then, the extracted top k topical terms from each subtopic are combined to form the biological explanation of each gene cluster. Experimental results demonstrate that our system can obtain high quality clusters and provide informative key terms for the gene clusters.
- Hu X., Integration of Cluster Ensemble and Text Summarization for Gene Expression Analysis, in Proceedings of IEEE 2004 Symposium on Bioinformatics and Bioengineering, 251--259, May 19-21, 2004, Taiwan (IEEE BIBE 2004) Google Scholar
Digital Library
- Kamal Nigam, Andrew McCallum, Sebastian Thrun and Tom Mitchell. Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2/3). pp. 103--134. 2000. Google ScholarDigital Library
Index Terms
- Integration of cluster ensemble and EM based text mining for microarray gene cluster identification and annotation
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Microarray gene cluster identification and annotation through cluster ensemble and EM-based informative textual summarization
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