Abstract
In this paper we present results of analysis if (and how) the functional similarity of genes can be compared to the similarity resulting from raw experimental data. We assume that information provided by Gene Ontology database can be regarded as an expert knowledge on genes and their function and therefore it should be correlated with genes similarity obtained based on analysis of raw expression data. We analyse several different measures of genes similarities in the Gene Ontology (GO) domain and compare the obtained results with the genes similarities observed in the expression level domain. We perform the analysis on three datasets on different characteristics. We shows that there is no single measure which gives the best results in all cases, and the choice of appropriate gene similarity measure depends on sets characteristics. In most cases, the best results are obtained by Avg-sum gene similarity measure in combination with Path–length GO terms similarity measure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ashburner, M., Ball, C., Blake, J., Botstein, D., Butler, H., Cherry, J., Davis, A., Dolinski, K., Dwight, S., Eppig, J., Harris, M., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J., Richardson, J., Ringwald, M., Rubin, G., Sherlock, G.: Gene ontology: tool for the unification of biology. Nature Genetics 25, 25–29 (2000)
Azuaje, F., Wang, H., Bodenreider, O.: Ontology-driven similarity approaches to supporting gene functional assessment. In: Proceedings of the ISMB 2005 SIG Meeting on Bio-ontologies, pp. 9–10 (2005)
Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America, 1998 95, 14863–14868 (1998)
Hisham, A.M., Anurag, N.: Comparison of four similarity measures based on GO annotations for gene clustering. In: Proceedings of IEEE Symposium on Computers and Communications, pp. 531–536 (2008)
Iyer, V., Eisen, M., Ross, D., Schuler, G., Moore, T., Lee, J., Trent, J., Staudt, L., Hudson, J., Boguski, M., Lashkari, D., Shalon, D., Botstein, D., Brown, P.: The transcriptional program in the response of human fibroblasts to serum. Science 283(5398), 83–87 (1999)
Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical ontology. In: Proceedings of the International Conference on Research in Computational Linguistics, pp. 19–33 (1997)
Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, pp. 296–304 (1998)
Pesquita, C., Faria, D., Falcão, A., Lord, P., Couto, F.: Semantic similarity in biomedical ontologies. PLoS Computational Biology 5, 1–12 (2009)
Resnick, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11, 95–130 (1999)
Sevilla, J.L., Segura, V., Podhorski, A., Guruceaga, E., Mato, J.M., Martinez-Cruz, L.A., Corrales, F.J., Rubio, A.: Correlation between gene expression and GO semantic similarity. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2, 330–338 (2005)
Sikora, M., Gruca, A.: Induction and selection of the most interesting gene ontology based multiattribute rules for descriptions of gene groups. Pattern Recognition Letters 32(2), 258–269 (2011)
Wang, H., Azuaje, F., Bodenreider, O., Dopazo, J.: Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationships. In: Proceedings of IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology CIBCB 2004, pp. 25–31 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gruca, A., Kozielski, M. (2011). Correlation of Genes Similarity Measures Based on GO Terms Similarity and Gene Expression Values. In: Czachórski, T., Kozielski, S., Stańczyk, U. (eds) Man-Machine Interactions 2. Advances in Intelligent and Soft Computing, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23169-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-23169-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23168-1
Online ISBN: 978-3-642-23169-8
eBook Packages: EngineeringEngineering (R0)