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Semi-supervised Approach for Finding Cancer Sub-classes on Gene Expression Data

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Part of the Lecture Notes in Computer Science book series (LNBI,volume 6268)

Abstract

The analysis of cancer gene expression is intrinsically a semi- supervised problem, as one is interested in building a classifier for diagnosis, but also on finding new sub-classes of cancer. We propose here a method for Mixture Discriminant Analysis (MDA), which can simultaneously detect sub-classes of cancer and perform classification. We evaluate the method on 10 gene expression data sets. MDA not only improved the classification in some of these data sets, as it detected some known and putative sub-classes of cancer.

Keywords

  • cancer gene expression
  • mixture discriminant analysis
  • semi- supervised learning
  • constraint based mixture estimation

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Ribeiro, C., de Assis T. de Carvalho, F., Costa, I.G. (2010). Semi-supervised Approach for Finding Cancer Sub-classes on Gene Expression Data. In: Ferreira, C.E., Miyano, S., Stadler, P.F. (eds) Advances in Bioinformatics and Computational Biology. BSB 2010. Lecture Notes in Computer Science(), vol 6268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15060-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-15060-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15059-3

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