6th International Conference on Practical Applications of Computational Biology & Bioinformatics pp 121-127 | Cite as
Visual Analysis Tool in Comparative Genomics
Conference paper
Abstract
Detecting regions with mutations associated with different pathologies is an important step in selecting relevant genes, proteins or diseases. The corresponding information of the mutations and genes is distributed in different public sources and databases, so it is necessary to use systems that can contrast different sources and select conspicuous information. This work presents a visual analysis tool that automatically selects relevant segments and the associated genes or proteins that could determine different pathologies.
Keywords
Comparative Genomic Hybridization Gain Function Detect Copy Number Variation Comparative Genomic Hybridization Microarrays Relevant Segment
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.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Aha, D., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
- 2.Breiman, L., Fried, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth International Group. (1984)Google Scholar
- 3.Chen, W., Erdogan, F., Ropers, H., Lenzner, S., Ullmann, R.: CGHPRO- a comprehensive data analysis tool for array CGH. BMC Bioinformatics 6(85), 299–303 (2005)Google Scholar
- 4.Cohen, W.W.: Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann, San Francisco (1995)Google Scholar
- 5.De Haan, J.R., Bauerschmidt, S., van Schaik, R.C., Piek, E., Buydens, L.M.C., Wehrens, R.: Robust ANOVA for microarray data. Chemometrics and Intelligent Laboratory Systems 98(1), 38–44 (2009)CrossRefGoogle Scholar
- 6.Duda, R.O., Hart, P.: Pattern classification and Scene Analysis. John Wisley & Sons, New York (1973)MATHGoogle Scholar
- 7.Holmes, G., Hall, M., Prank, E.: Generating Rule Sets from Model Trees. Advanced Topics in Artificial Intelligence 1747(1999), 1–12 (2007)Google Scholar
- 8.Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–91 (1993)MATHCrossRefGoogle Scholar
- 9.Kim, S.Y., Nam, S.W., Lee, S.H., Park, W.S., Yoo, N.J., Lee, J.Y., Chung, Y.J.: ArrayCyGHt, a web application for analysis and visualization of array-CGH data. Bioinformatics 21(10), 2554–2555 (2005)CrossRefGoogle Scholar
- 10.Lingjaerde, O.C., Baumbush, L.O., Liestol, K., Glad, I.K., Borresen-Dale, A.L.: CGH-explorer, a program for analysis of array-CGH data. Bioinformatics 21(6), 821–822 (2005)CrossRefGoogle Scholar
- 11.Mantripragada, K.K., Buckley, P.G., Diaz de Stahl, T., Dumanski, J.P.: Genomic microarrays in the spotlight. Trends Genetics 20(2), 87–94 (2004)CrossRefGoogle Scholar
- 12.Menten, B., Pattyn, F., De Preter, K., Robbrecht, P., Michels, E., Buysse, K., Mortier, G., De Paepe, A., van Vooren, S., Vermeesh, J., et al.: ArrayCGHbase: an analysis platform for comparative genomic hybridization microarrays. BMC Bioinformatics 6(124), 179–187 (2006)Google Scholar
- 13.Pinkel, D., Albertson, D.G.: Array comparative genomic hybridization and its applications in cancer. Nature Genetics 37, 11–17 (2005)CrossRefGoogle Scholar
- 14.Quinlan, J.R.: C4.5: Programs For Machine Learning. Morgan Kaufmann Publishers Inc. (1993)Google Scholar
- 15.Rosa, P., Viara, E., Hupé, P., Pierron, G., Liva, S., Neuvial, P., Brito, I., Lair, S., Servant, N., Robine, N., Manié, E., Brennetot, C., Janoueix-Lerosey, I., Raynal, V., Gruel, N., Rouveirol, C., Stransky, N., Stern, M., Delattre, O., Aurias, A., Radvanyi, F., Barillot, E.: Visualization and analysis of array-CGH, transcriptome and other molecular profiles. Bioinformatics 22(17), 2066–2073 (2006)CrossRefGoogle Scholar
- 16.Wang, P., Young, K., Pollack, J., Narasimham, B., Tibshirani, R.: A method for callong gains and losses in array CGH data. Biostat. 6(1), 45–58 (2005)MATHCrossRefGoogle Scholar
- 17.Xia, X., McClelland, M., Wang, Y.: WebArray, an online platform for microarray data analysis. BMC Bionformatics 6(306), 1737–1745 (2005)Google Scholar
- 18.Ylstra, B., Van den Ijssel, P., Carvalho, B., Meijer, G.: BAC to the future! or oligonucleotides: a perspective for microarray comparative genomic hybridization (array CGH). Nucleic Acids Research 34, 445–450 (2006)CrossRefGoogle Scholar
- 19.Yue, S., Wang, C.: The influence of serial correlation on the Mann-Whitney test for detecting a shift in median. Advances in Water Resources 25(3), 325–333 (2002)CrossRefGoogle Scholar
- 20.
- 21.Kruskal, W., Wallis, W.: Use of ranks in one-criterion variance analysis. Journal of American Statistics Association (1952)Google Scholar
- 22.Kenney, J.F., Keeping, E.S.: Mathematics of Statistics, Pt. 2, 2nd edn. Van Nostrand, Princeton (1951)Google Scholar
- 23.Van de Wiel, M.A., Kim, K.I., Vosse, S.J., Van Wieringen, W.N., Wilting, S.M., Ylstra, B.: CGHcall: calling aberrations for array CGH tumor profiles. Bioinformatics 23(7), 892–894 (2007)CrossRefGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2012