An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Correlation between Gene Expression and GO Semantic Similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Electricity based external similarity of categorical attributes
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Genes responding similarly to changing conditions are believed to be functionally related. Identification of such functional relations is crucial for annotation of unknown genes as well as the exploration of the underlying regulatory program. Gene expression profiling experiments provide noisy datasets about how cells respond to different experimental conditions. One way of analyzing these datasets is the identification of gene groups with similar expression patterns. A prevailing technique to find gene pairs with correlated expression profiles is to use linear measures like Pearson's correlation coefficient or Euclidean distance. Similar genes are later compiled into a co-expression network to explore the system-level functionality of genes. However, the noise inherent in microarray datasets reduces the sensitivity of these measures and produces many spurious pairs with no real biological relevance. In this paper, we explore an extrinsic way of calculating similarity of two genes based on their relations with other genes. We show that `similar' pairs identified by extrinsic measures overlap better with known biological annotations available in the Gene Ontology database. Our results also indicate that extrinsic measures are useful in enhancing the quality of co-expression networks and their functional subnetworks.