Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Incorporating Ontology-Driven Similarity Knowledge into Functional Genomics: An Exploratory Study
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
A knowledge-driven approach to cluster validity assessment
Bioinformatics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Ontology - supported machine learning and decision support in biomedicine
DILS'07 Proceedings of the 4th international conference on Data integration in the life sciences
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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.