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Discriminative Pattern Discovery on Biological Networks pp 31–45Cite as

Discriminating Graph Pattern Mining from Gene Expression Data

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Abstract

Here we consider the problem of mining gene expression data in order to single out interesting features characterizing healthy/ unhealthy samples of an input dataset. The presented approach is based on a network model of the input gene expression data, where there is a labeled graph for each sample. This is the first attempt to build a different graph for each sample and, then, to have a database of graphs for representing a sample set. The main goal is that of singling out interesting differences between healthy and unhealthy samples, through the extraction of discriminative patterns among graphs belonging to the two different sample sets. Differently from the other approaches presented in the literature, this technique is able to take into account important local similarities, and also collaborative effects involving interactions between multiple genes. In particular, edge-labeled graphs are employed and the discriminative power of a pattern is measured on the basis of edge weights, which are representative of how much relevant is the co-expression between two genes.

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Notes

  1. 1.

    Since there is a one-to-one correspondence between an individual and its representing tuple, for the sake of simplicity, we employ the same symbol t to denote both the individual and its corresponding tuple in the dataset.

  2. 2.

    The reader is referred to Sect. 4.4.2 for the details.

  3. 3.

    Note that, due to the symmetry of Eq. (4.2), the same line of reasoning can be followed to find, given values \(\rho _0\) and \(x_0\), the values of y such that the value of \(\rho \) solution of Eq. (4.2) is larger than \(\rho _0\).

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Authors and Affiliations

  1. University of Calabria, Calabria, Cosenza, Italy

    Fabio Fassetti

  2. University of Palermo, Palermo, Italy

    Simona E. Rombo

  3. University of Calabria, Calabria, Cosenza, Italy

    Cristina Serrao

Authors
  1. Fabio Fassetti
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  2. Simona E. Rombo
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  3. Cristina Serrao
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Correspondence to Fabio Fassetti .

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Fassetti, F., Rombo, S.E., Serrao, C. (2017). Discriminating Graph Pattern Mining from Gene Expression Data. In: Discriminative Pattern Discovery on Biological Networks. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-63477-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-63477-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63476-0

  • Online ISBN: 978-3-319-63477-7

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