Abstract
In Supervised Learning it is assumed that is straightforward to obtained labeled data. However, in reality labeled data can be scarce or expensive to obtain. Active Learning (AL) is a way to deal with the above problem by asking for the labels of the most “informative” data points. We propose a novel AL method based on wavelet analysis, which pertains especially to the large number of dimensions (i.e. examined genes) of microarray experiments. DNA Microarray expression experiments permit the systematic study of the correlation of the expression of thousands of genes. We have applied our method on such data sets with encouraging results. In particular we studied data sets concerning: Small Round Blue Cell Tumours (4 types), Leukemia (2 types) and Lung Cancer (2 types).
The work presented in this paper has been undertaken in the framework of the OPTOPOIHSH project (PLHRO/0104/04 – Development of knowledge-based Visual Attention models for Perceptual Video Coding) funded by the Cyprus Research Promotion Foundation, Framework Programme for Research and Technological Development 2003-2005.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bergeron, B.: Bioinformatics Computing, ch.6, pp. 222–231. Prentice Hall, Englewood Cliffs (2004)
Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active learning with statistical models. In: Tesauro, G., Touretzky, D., Leen, T. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 705–712. The MIT Press, Cambridge (1995)
MacKay, D.: Information-based objective functions for active data selection. Neural Computation 4, 590–604 (1992)
Hasenjäger, M., Ritter, H.: Active learning with local models. Neural Processing Letters 7, 107–117 (1998)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. In: Langley, P. (ed.) Proceedings of ICML-00, 17th International Conference on Machine Learning, pp. 999–1006. Morgan Kaufmann Publishers, San Francisco (2000)
Li, T., Li, Q., Zhu, S., Ogihara, M.: A survey on wavelet applications in data mining. SIGKDD Explorations 4, 49–68 (2003)
Liò, P.: Wavelets in bioinformatics and computational biology: state of art and perspectives. Bioinformatics 19, 2–9 (2003)
Quackenbush, J.: Computational Analysis of Microarray Data. Nature Reviews 2, 418–428 (2001)
Khan, J., Wei, J., Ringer, M., Saal, L., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C., Peterson, C., Meltzer, P.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural network. Nature Medicine 7, 673–679 (2001)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science (1999)
Gordon, G., Jensen, R., Hsiao, L., Gullans, S., Blumenstock, J., Ramaswamy, S., Richard, W., Sugarbaker, D., Bueno, R.: Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research, 4963–4967 (2002)
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
Vogiatzis, D., Tsapatsoulis, N. (2006). Active Learning with Wavelets for Microarray Data. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds) Fuzzy Logic and Applications. WILF 2005. Lecture Notes in Computer Science(), vol 3849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11676935_31
Download citation
DOI: https://doi.org/10.1007/11676935_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32529-1
Online ISBN: 978-3-540-32530-7
eBook Packages: Computer ScienceComputer Science (R0)