NDLI: Validating Clustering for Gene Expression Data Based on Semantic Distance of Gene Ontology Terms

Content Provider	IEEE Xplore Digital Library
Author	Feizhen Wu Wenli Ma Mei Wang Qilong Chen Wenling Zheng
Copyright Year	2008
Description	Author affiliation: Bioelectornic Center, Shanghai Univ., Shanghai (Feizhen Wu; Wenli Ma)
Abstract	Clustering algorithms for gene expression data attempt to partition the gene expression data into groups, which exhibits similar patterns of variation in expression level. Many clustering algorithms have been proposed, but little guidance is available to evaluate the clustering result from biological meaning. We developed a new algorithm to measure semantic distance between Gene Ontology (GO) terms. Based on this algorithm, we proposed a novel method to assess the biological predictive power of the clustering algorithms: among a cluster, the more similar the functions of genes are, the lower the semantic distance is. We applied the approach to evaluating hierarchical clustering algorithms for yeast cell and diabetes datasets, and successfully obtained the biological features of the gene clusters. We found the approach may contribute to achieve better clustering results.
Starting Page	706
Ending Page	709
File Size	296223
Page Count	4
File Format	PDF
ISBN	9781424417476
DOI	10.1109/ICBBE.2008.172
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fungi Data analysis Endocrine system Clustering algorithms Cells (biology) Ontologies Partitioning algorithms Diabetes Gene expression Biological materials
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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