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International Conference on Hybrid Artificial Intelligence Systems

HAIS 2016: Hybrid Artificial Intelligent Systems pp 673–684Cite as

TRIQ: A Comprehensive Evaluation Measure for Triclustering Algorithms

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9648)

Abstract

Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. Triclustering relaxes the constraints for grouping and allows genes to be evaluated under a subset of experimental conditions and a subset of time points simultaneously. The authors previously presented a genetic algorithm, TriGen, that finds triclusters of gene expression dasta. They also defined three different fitness functions for TriGen: \(MSR_{3D}\), LSL and MSL. In order to asses the results obtained by application of TriGen, a validity measure needs to be defined. Therefore, we present TRIQ, a validity measure which combines information from three different sources: (1) correlation among genes, conditions and times, (2) graphic validation of the patterns extracted and (3) functional annotations for the genes extracted.

Keywords

  • Triclustering
  • Validity measure
  • Genetic algorithms
  • Microarrays

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Acknowledgments

The authors thank financial support by the Spanish Ministry of Science and Technology, projects TIN2011-28956-C02-02 and TIN2014-55894-C2-1-R and Junta de Andalucía’s project P12-TIC-7528.

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Authors and Affiliations

  1. Department of Computer Science, University of Seville, Seville, Spain

    David Gutiérrez-Avilés & Cristina Rubio-Escudero

Authors
  1. David Gutiérrez-Avilés
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  2. Cristina Rubio-Escudero
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Corresponding author

Correspondence to Cristina Rubio-Escudero .

Editor information

Editors and Affiliations

  1. Universidad Pablo de Olavide, Sevilla, Spain

    Francisco Martínez-Álvarez

  2. Universidad Pablo de Olavide, Sevilla, Spain

    Alicia Troncoso

  3. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Gutiérrez-Avilés, D., Rubio-Escudero, C. (2016). TRIQ: A Comprehensive Evaluation Measure for Triclustering Algorithms. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_56

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  • DOI: https://doi.org/10.1007/978-3-319-32034-2_56

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