Abstract
In gene set focused knowledge-based analysis we assume that genes from the same functional gene set have similar transcription profiles. We compared the distributions of similarity scores of gene transcription profiles between genes from the same gene sets and genes chosen at random. In line with previous research, our results show that transcription profiles of genes from the same gene sets are on average indeed more similar than random transcription profiles, although the differences are slight. We performed the experiments on 35 human cancer data sets, with KEGG pathways and BioGRID interactions as gene set sources. Pearson correlation coefficient and interaction gain were used as association measures.
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References
Anastassiou, D.: Computational analysis of the synergy among multiple interacting genes. Mol. Syst. Biol. 3(83) (February 2007)
Ashburner, M., Ball, C., Blake, J., Botstein, D., Butler, H., Cherry, J., Davis, A., Dolinski, K., Dwight, S., Eppig, J., et al.: Gene ontology: tool for the unification of biology. Nature genetics 25(1), 25–29 (2000)
Barrett, T., Troup, D.B., Wilhite, S.E., Ledoux, P., Rudnev, D., Evangelista, C., Kim, I.F., Soboleva, A., Tomashevsky, M., Edgar, R.: NCBI GEO: mining tens of millions of expression profiles–database and tools update. Nucl. Acids Res. 35, D760–765 (2007)
Bellazzi, R., Zupan, B.: Towards knowledge-based gene expression data mining. Journal of Biomedical Informatics 40(6), 787–802 (2007)
Bhardwaj, N., Lu, H.: Correlation between gene expression profiles and protein–protein interactions within and across genomes. Bioinformatics 21(11), 2730 (2005)
Demšar, J., Zupan, B., Leban, G.: Orange: From experimental machine learning to interactive data mining, white paper (2004)
Fraser, H., Hirsh, A., Wall, D., Eisen, M.: Coevolution of gene expression among interacting proteins. Proceedings of the National Academy of Sciences of the United States of America 101(24), 9033 (2004)
Jakulin, A., Bratko, I.: Analyzing attribute dependencies. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 229–240. Springer, Heidelberg (2003)
Jansen, R., Greenbaum, D., Gerstein, M.: Relating whole-genome expression data with protein-protein interactions. Genome Research 12(1), 37 (2002)
Jelizarow, M., Guillemot, V., Tenenhaus, A., Strimmer, K., Boulesteix, A.: Over-optimism in bioinformatics: an illustration. Bioinformatics 26(16), 1990 (2010)
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., Hirakawa, M.: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Research 38(Database issue), D355 (2010)
Lee, E., Chuang, H.Y., Kim, J.W., et al.: Inferring pathway activity toward precise disease classification. PLoS Comput. Biol. 4(11), e1000217 (2008)
Lee, H., Hsu, A., Sajdak, J., Qin, J., Pavlidis, P.: Coexpression analysis of human genes across many microarray data sets. Genome Research 14(6), 1085 (2004)
Mramor, M., Toplak, M., Leban, G., Curk, T., Demšar, J., Zupan, B.: On utility of gene set signatures in gene expression-based cancer class prediction. In: Machine Learning in Systems Biology, p. 65 (2009)
Nam, D., Kim, S.Y.: Gene-set approach for expression pattern analysis. Brief Bioinform 9(3), 189–197 (2008)
Sheskin, D.: Handbook of parametric and nonparametric statistical procedures. CRC Pr I Llc (2004)
Stark, C., Breitkreutz, B., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a general repository for interaction datasets. Nucleic Acids Research 34(suppl. 1), 535 (2006)
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Toplak, M., Curk, T., Zupan, B. (2011). Similarity of Transcription Profiles for Genes in Gene Sets. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_41
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DOI: https://doi.org/10.1007/978-3-642-20267-4_41
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