Abstract
Identifying gene function has many useful applications especially in Gene Therapy. Identifying gene function based on gene expression data is much easier in prokaryotes than eukaryotes due to the relatively simple structure of prokaryotes. That is why tissue-specific expression is the primary tool for identifying gene function in eukaryotes. However, recent studies have shown that there is a strong learnable correlation between gene function and gene expression. This paper outlines a new approach for gene function prediction in mouse. The prediction mechanism depends on using Artificial Neural Networks (NN) to predict gene function based on quantitative analysis of gene co-expression. Our results show that neural networks can be extremely useful in this area. Also, we explore clustering of gene functions as a preprocessing step for predicting gene function.
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
Vinayagam, A., König, R., Moormann, J., Schubert, F., Eils, R., Glatting, K., Suhai, S.: Applying Support Vector Machines for Gene Ontology Based Gene Function Prediction. BMC Bioinformatics 5(116) (2004)
Baldi, P., Brunak, S.: Bioinformatics a Machine Learning Approach, 2nd edn. MIT Press, Cambridge (2001)
Beer, M.A., Tavazoie, S.: Predicting Gene Expression from Sequence. Cell 16 117(2), 185–198 (2004)
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Elis Horwood, London (1994)
Morrison, K.E., Daniels, R.J., Campbell, L., McPherson, J., Davies, K.E.: Human Molecular Genetics 2 (1993)
Haykin, S.: Neural Networks: A Comprehensive Introduction. Prentice Hall, New Jersey (1999)
Le Cun, Y., Touresky, D., Hinton, G., Sejnowski, T.: A Theoretical Framework for Backpropagation. The Connectionist Models Summer School, pp. 21–28 (1988)
Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Furey, T.S., Ares Jr., M., Haussler, D.: Knowledge-Based Analysis of Microarray Gene Expression Data Using Support Vector Machines. Proceedings of the National Academy of Sciences 97(1), 262–267 (1997)
Mitchell, T.M.: Machine Learning. McGraw-Hill Inc., USA (1997)
NCBI COGs database, http://ncbi.nlm.nih.gov/COG/
King, O.D., Foulger, R.E., Dwight, S.S., White, J.V., Roth, F.P.: Predicting Gene Function from Patterns of Annotation. Genome Research 13(5), 896–904 (2003)
Shenouda, E.: A Quantitative Comparison of Different MLP Activation Functions in Classification. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 849–857. Springer, Heidelberg (2006)
The Functional Landscape of Mouse Gene Expression, http://hugheslab.med.utoronto.ca/Zhang
Wang, D., Huang, G.: Protein Sequence Classification Using Extreme Learning Machine. In: IJCNN 2005, Montréal, vol. 3, pp. 1406–1411 (2005)
Werbos, P.J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Doctoral Thesis, Applied Mathematics. Harvard University, Boston (1974)
Zhang, W., Morris, Q.D., Chang, R., Shai, O., Bakowski, M.A., Mitsakakis, N., Mohammad, N., Robinson, M.D., Zirngibl, R., Somogyi, E., Laurin, N., Eftekharpour, E., Sat, E., Grigull, J., Pan, Q., Peng, W.T., Krogan, N., Greenblatt, J., Fehlings, M., Kooy, D.V., Aubin, J., Bruneau, B.G., Rossant, J., Blencowe, B.J., Frey, B.J., Hughes, T.R.: The Functional Landscape of Mouse Gene Expression. Journal of Biology 3, Article 21 (2004)
Zurada, J.M.: Introduction to Artificial Neural Systems. PWS Publishing, Boston (1999)
Mateos, A., Dopazo, J., Jansen, R., Tu, Y., Gerstein, M., Stolovitzky, G.: Systematic Learning of Gene Functional Classes From DNA Array Expression Data by Using Multilayer Perceptrons. Genome Research 12, 1703–1715 (2002)
Kohonen, T.: Self-Organizing and Associative Memory, 3rd edn. Springer, New York (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shenouda, E.A., Morris, Q., Bonner, A.J. (2006). Connectionist Approaches for Predicting Mouse Gene Function from Gene Expression. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_32
Download citation
DOI: https://doi.org/10.1007/11893028_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
eBook Packages: Computer ScienceComputer Science (R0)