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Incorporating Biological Domain Knowledge into Cluster Validity Assessment

  • Nadia Bolshakova
  • Francisco Azuaje
  • Pádraig Cunningham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

This paper presents an approach for assessing cluster validity based on similarity knowledge extracted from the Gene Ontology (GO) and databases annotated to the GO. A knowledge-driven cluster validity assessment system for microarray data was implemented. Different methods were applied to measure similarity between yeast genes products based on the GO. This research proposes two methods for calculating cluster validity indices using GO-driven similarity. The first approach processes overall similarity values, which are calculated by taking into account the combined annotations originating from the three GO hierarchies. The second approach is based on the calculation of GO hierarchy-independent similarity values, which originate from each of these hierarchies. A traditional node-counting method and an information content technique have been implemented to measure knowledge-based similarity between genes products (biological distances). The results contribute to the evaluation of clustering outcomes and the identification of optimal cluster partitions, which may represent an effective tool to support biomedical knowledge discovery in gene expression data analysis.

Keywords

Gene Ontology Validity Index Cluster Validity Index Gene Expression Data Analysis Saccharomyces Genome Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nadia Bolshakova
    • 1
  • Francisco Azuaje
    • 2
  • Pádraig Cunningham
    • 1
  1. 1.Department of Computer ScienceTrinity College DublinIreland
  2. 2.School of Computing and MathematicsUniversity of UlsterJordanstownUK

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