Neighborhood-Based Clustering of Gene-Gene Interactions

  • Norberto Díaz–Díaz
  • Domingo S. Rodríguez–Baena
  • Isabel Nepomuceno
  • Jesús S. Aguilar–Ruiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


In this work, we propose a new greedy clustering algorithm to identify groups of related genes. Clustering algorithms analyze genes in order to group those with similar behavior. Instead, our approach groups pairs of genes that present similar positive and/or negative interactions. Our approach presents some interesting properties. For instance, the user can specify how the range of each gene is going to be segmented (labels). Some of these will mean expressed or inhibited (depending on the gradation). From all the label combinations a function transforms each pair of labels into another one, that identifies the type of interaction. From these pairs of genes and their interactions we build clusters in a greedy, iterative fashion, as two pairs of genes will be similar if they have the same amount of relevant interactions. Initial two–genes clusters grow iteratively based on their neighborhood until the set of clusters does not change. The algorithm allows the researcher to modify all the criteria: discretization mapping function, gene–gene mapping function and filtering function, and provides much flexibility to obtain clusters based on the level of precision needed.

The performance of our approach is experimentally tested on the yeast dataset. The final number of clusters is low and genes within show a significant level of cohesion, as it is shown graphically in the experiments.


Mapping Function Gene Interaction Memetic Algorithm Discretized Matrix Yeast Dataset 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Norberto Díaz–Díaz
    • 1
  • Domingo S. Rodríguez–Baena
    • 1
  • Isabel Nepomuceno
    • 1
  • Jesús S. Aguilar–Ruiz
    • 1
  1. 1.BioInformatics Group SevilleSeville and Pablo de Olavide UniversitySpain

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